DETECTION OF AIR LEAKAGE IN BOILER · PDF fileABSTRACT: Leakage detection ... using PIC microcontroller it would control the levels of pressure. The simulation model ... a surface

National Conference on Research Advances in Communication, Computation, Electrical Science and Structures (NCRACCESS-2015)

ISSN: 2348 - 8379 www.internationaljournalssrg.org Page 1

DETECTION OF AIR LEAKAGE IN BOILER RECTANGULAR

DUCT USING SIMULATION

P.Jayapriya

1 , P.Hari krishnan

2

1,2Department of electrical and electronics engineering,

Anna university regional centre, Coimbatore, India.

ABSTRACT: Leakage detection is one of

the most important design objectives in

large scale industries. Efficient and reliable

operation is important in boiler. Corrosion,

erosion and thermal stress can cause holes

and cracks resulting in a flow of water or

steam into the combustion side of the boiler.

The leakages can be located in either the

water or steam tubes. This project describes

the new approach to the detection of air

leakages in boiler rectangular duct by using

pressure sensor and its application in

practical use. The impacts of various factors

on the performance of the detection system

were discussed, including different sensing

elements. The automation was done by

using PIC microcontroller it would control

the levels of pressure. The simulation model

of the project was designed using proteus

software. The leakage occur in boiler duct,

it would predicated by pressure sensor and

LED. The results were obtained by using

LCD display.

Key words: Air leakage, boiler, rectangular

duct, pressure sensor.

I INTRODUCTION

A boiler or steam generator is a device

used to create steam by applying heat energy

to water. Any appliance that is constantly

exposed to water is prone to leaks, and steam

boilers are no exception. Care and

maintenance can go a long way toward

preventing leaks, but the corrosive effect of

water can only be held off for so long.

Leakage can occur at a number of points

inside and on the surface of steam boiler

machinery. The conventional method of

detecting tube leaks such as monitoring boiler

make up water, mass balancing or merely

depending on the human ear to recognise a

sound change, are not sufficiently sensitive for

large boilers. By using these methods the leak

is often big enough to have already caused

serious consequential damage sometimes to an

entire boiler face. Boiler tube leakage is a

major cause of outage and as consequence

power generation loss in thermal power plants

is huge. Leakage detection in recovery boilers

is important to avoid severe damage of

equipment .The walls of the furnace are

containing evaporating water with high

pressure.

Boiler tube leaks must be detected very

early, otherwise leaking steam may further

damage adjacent costly parts due to heavy

impact. Maintenance cost to secondary

damage from boiler tube leaks is very high

and repairing the damage requires several

weeks to complete. Due to the substantial

costs associated with any forced outage, it is

imperative to perform routine inspections. In

this manner, conditions with the potential to

result in failures can be identified, monitored

and addressed before they do result in failures.

The quantity of makeup water and hissing

sound emitted by leaking steam is detected by

human ears at the boiler. There are a number

of systems for detection of Tube leakage

available in both conventional boilers and

recovery boiler. The leakage of power

situation’s tubes like Pitot tube, water wall

tubes, super heater tubes, repeater tubes and

economizer tube, the detections are traditional

method.

The goal of this work can be briefed as

follows:

Design an efficient boiler

Early detection of leakage helps to

avoid damage

Improve boiler output.



.

II PRINCIPLE OF AIR LEAK TEST

The pressure used in the air leak test is

the atmospheric pressure. Atmospheric

pressure is the pressure generated by the

weight of air. As we live on earth,

atmospheric pressure is constantly applied

to us. The atmospheric pressure at sea level

is 101325Pa (1013hPa) which means that

we are pressed by a force of 1013hPa.The

level of atmospheric pressure changes

depending on the altitude. The

uncomfortable feeling we get on our

eardrum when we ride in an elevator to the

highest floor of a high-rise building is the

effect of the atmospheric pressure.

When the atmospheric pressure is

high, the amount of air per unit volume is

high and when the atmospheric pressure is

low, the amount of air per unit volume is

low. Air flows from where the atmospheric

pressure is high to where it is low and tries

to maintain a balanced condition of

atmospheric pressure. The air leak test is

performed using this transfer of air by

difference in pressure.

When air is charged to the work and

pressure inside the work increases, the

pressure difference between the inside and

outside of the work will be generated.

When the work has leak by a hole, the air

inside the work will flow outside and the

pressure inside the work will decrease. The

change of pressure inside the work is

monitored and a leak is detected through

the air leak test.

A NORMAL FACTOR OF.LEAKAGE

Types of leak openings include a

puncture, gash, or other corrosion hole,

very tiny pinhole leak crack or micro

crack, or in adequate sealing between

components or parts joined together. In

many cases, the location of a leak can be

determined by seeing material drip out at a

certain place. In some cases, it may known

or suspected there is a leak, but even the

location of the leak is not known. Since

leak openings are often so irregular, leaks

are sometimes sized by the leakage rate, as

in volume of fluid leaked per time, rather

than the size of the opening.

Leaks can occur or develop in many

different kinds of household, building,

vehicle, marine, aircraft, or industrial fluid

systems, whether the fluid is a gas or

liquid. Leaks in vehicle hydraulic systems

such as brake or power steering lines could

cause out leakage of brake or power

steering fluid resulting in failure of the

brakes, power steering, or other hydraulic

system. Also possible are leaks of engine

coolant - particularly in the radiator and at

the water pump seal, motor oil

and refrigerant in the conditioning system.

Some of these vehicle fluids have different

colours to help identify the type of leaking

fluid.

A system holding a full or

partial vacuum may have a leak causing in

leakage of air from the outside. Hazmat

procedures and/or teams may become

involved when leakage or spillage of

hazardous materials occurs. However, even

leakage of steam can be dangerous because

of the high temperature and energy of the

steam..

There can be numerous causes of

leaks. Leaks can occur from the outset

even during construction or initial

manufacture/assembly of fluid

systems. Pipes, tubing, valves, fittings, or

other components may be improperly

joined or welded together. Components

with threads may be improperly screwed

together .Often leaks are the result of

deterioration of materials from wear or

aging, such as rusting or other corrosion or

decomposition or

similar polymer materials used

as gaskets or other seals. For example,

wearing out of faucet washers causes water

to leak at the faucets. Cracks may result

from either outright damage, or wearing

out by stress such as fatigue failure or

corrosion such as cracking. Wearing out of

a surface between a disk and its seat in a

valve could cause a leak between ports

(valve inlets or outlets). Wearing out of

packing around a turning valve stem or

rotating centrifugal pump shaft could

develop into fluid leakage into the

environment. Similarly, wearing out of

seals or packing around piston-driven

pumps could also develop into out leakage

to the environment.

http://en.wikipedia.org/wiki/Corrosion

http://en.wikipedia.org/wiki/Hydraulic

http://en.wikipedia.org/wiki/Brake

http://en.wikipedia.org/wiki/Radiator

http://en.wikipedia.org/wiki/Water_pump

http://en.wikipedia.org/wiki/Refrigerant

http://en.wikipedia.org/wiki/Vacuum

http://en.wikipedia.org/wiki/Steam

http://en.wikipedia.org/wiki/Construction

http://en.wikipedia.org/wiki/Pipe_(material)

http://en.wikipedia.org/wiki/Tube_(fluid_conveyance)

http://en.wikipedia.org/wiki/Valve

http://en.wikipedia.org/wiki/Pipe_and_plumbing_fitting

http://en.wikipedia.org/wiki/Polymer

http://en.wikipedia.org/wiki/Gasket

http://en.wikipedia.org/wiki/Seal_(mechanical)

http://en.wikipedia.org/wiki/Faucet

http://en.wikipedia.org/wiki/Washer_(hardware)

http://en.wikipedia.org/wiki/Fatigue_(material)

http://en.wikipedia.org/wiki/Centrifugal_pump



The pressure difference between both

sides of the leak can affect the movement

of material through the leak. Fluids will

commonly move from the higher pressure

side to the lower pressure side .The fluid

pressures on both sides include

the hydrostatic pressure, which is pressure

due to the weight from the height of fluid

level above the leak.

A smaller leak from a corrosion hole

can cause erosion on tubes close to the

leaking tube. The erosion can then lead to a

larger tube rupture with an extensive

leakage flow as result. The magnitude of a

leakage can be varying. Leakages have in

other models been simulated with flows

from 0.2 kg/s. Early-warning Leakage

detection by analysing the mass-balance on

the steam-side of the boiler is only possible

when a large leakage flow is present. This

is due to relatively low measurement

Precision on feed-water and steam-flow in

comparison to a leakage flow.

A leakage can also be detected on the

combustion side by indirect calculations of

mass-flows, but the precision is lower with

this method compared to the method

exploiting the mass-balance on the steam-

side. By combining the two balances,

indications on a leakage can be considered

from both the steam-side and the

combustion side.

B BOILER TUBE LEAK DETECTION

As a leak develops in a boiler tube,

turbulence by escaping fluid generates

pressure waves within the contained fluid

itself, throughout the flue gas into which

the fluid is escaping, and within the

container structure. These are commonly

referred to as airborne, and structure-borne

acoustic waves, respectively. To detect

leaks, the energy associated with these

mechanical waves are converted into

electrical signals with a variety of dynamic

pressure transducers (sensors) that are in

contact with the medium of interest.

Several methods of signal processing are

available that allow the voltages generated

by these sensors to be evaluated for the

presence of a leak. As mentioned above,

leaks in a boiler tube generate sound waves

in three media. The decision regarding

which types of acoustic waves are most

reliably detected is important from both

functional and economical considerations.

. Airborne methods are well

established and have detected leaks as

much as a week before any other means

available. In airborne applications,

microphones or low frequency resonant

piezoelectric transducers are coupled by

hollow waveguides to the gaseous furnace

medium. Most leak detection system

usually attaches waveguides through

penetrations in inspection doors, unused

soot-blower ports or the casing. The

structure-borne method of leak detection

has found applications in valves and

pressurized pipelines.

C.AIR LEAKAGE IN RECTANGULAR

DUCT

Duct leakage tester is a diagnostic tool

designed to measure the air tightness of

forced air heating, ventilating and air-

conditioning (HVAC) ductwork. A duct

leakage tester consists of a calibrated fan

for measuring an air flow rate and a

pressure sensing device to measure the

pressure created by the fan flow. The

combinations of pressure and fan flow

measurements are used to determine the

ductwork air tightness. The air tightness of

ductwork is useful knowledge when trying

to improve energy conservation.

A basic duct leakage testing

system includes three components: a

calibrated fan, a register sealing system,

and a device to measure fan flow and

building pressure. Supply registers or

return air grills are sealed using adhesive

tapes, cardboard, or non-adhesive reusable

seals .One register or return is left

unsealed, and the calibrated fan is

connected to it. Pressure is monitored in

one of the branches of the ductwork while

the calibrated fan delivers air into the

system. As air is delivered into the

ductwork, pressure builds and forces air

out of all of the openings in the various

ductwork connections or through the seams

and joints of the furnace or air-conditioner.

The tighter the ductwork system (e.g.

fewer holes), the less air is needed from the

fan to create a change in the ductwork

pressure.

http://en.wikipedia.org/wiki/Hydrostatic_pressure

http://en.wikipedia.org/wiki/Weight

http://en.wikipedia.org/wiki/Energy_conservation



A duct leakage test can be performed

by either pressurizing or depressurizing the

ductwork. Ductwork that is outside the

building envelope, such as in an

unconditioned attic or crawlspace, should

be pressurized so as to not bring in

unwanted contaminants such as dust.

Figure1 Normal Rectangular duct

The volume of air which flows

through a closed window or door in a

given length of time as a result of the

difference in air pressure on its opposite

faces. In ductwork, air which escapes from

a joint, coupling, etc. The undesired

leakage or uncontrolled passage of air

from a ventilation system. The flow of

uncontrolled air through cracks or openings

in an enclosure within a building (such as a

HVAC plenum) or through the surfaces

which enclose the building.

D.BERNOULLI'S PRINCIPLE

Bernoulli's principle can be derived

from the principle of conservation of

energy. This states that, in a steady flow,

the sum of all forms of energy in a fluid

along a streamline is the same at all points

on that streamline. This requires that the

sum of kinetic energy, potential energy and

internal energy remains constant. Thus an

increase in the speed of the fluid –

implying an increase in both its dynamic

pressure and kinetic energy – occurs with a

simultaneous decrease in (the sum of) its

static pressure, potential energy and

internal energy. If the fluid is flowing out

of a reservoir, the sum of all forms of

energy is the same on all streamlines

because in a reservoir the energy per unit

volume (the sum of pressure and

gravitational potential ρ g h) is the same

everywhere.

Bernoulli's principle can also be

derived directly from Newton's 2nd law. If

a small volume of fluid is flowing

horizontally from a region of high pressure

to a region of low pressure, then there is

more pressure behind than in front. This

gives a net force on the volume,

accelerating it along the streamline.

Fluid particles are subject only to

pressure and their own weight. If a fluid is

flowing horizontally and along a section of

a streamline, where the speed increases it

can only be because the fluid on that

section has moved from a region of higher

pressure to a region of lower pressure; and

if its speed decreases, it can only be

because it has moved from a region of

lower pressure to a region of higher

pressure. Consequently, within a fluid

flowing horizontally, the highest speed

occurs where the pressure is lowest, and

the lowest speed occurs where the pressure

is highest. The principle is based on the

Bernoulli Equation where each term can be

interpreted as a form of pressure.

Calculation of velocity

Air Velocity

(1)

where

= Sensed pressure

difference (velocity pressure) in inches of

water column

D = Air density in lbs./ft.3

Cp = Pitot tube coefficient:

0.84

Air Density=

(2)

where

= Barometric pressure in

inches of mercury

T = Absolute Temperature

Flow in cubic feet per minute

equals duct cross sectional area in square

feet x air velocity in feet per minute. With

dry air at 29.9 inches of mercury, air

velocity can be read directly from

temperature correction charts on reverse.

Centres of

areas

Rectangular

Areas

http://en.wikipedia.org/wiki/Conservation_of_energy



http://en.wikipedia.org/wiki/Streamlines,_streaklines,_and_pathlines

http://en.wikipedia.org/wiki/Kinetic_energy

http://en.wikipedia.org/wiki/Potential_energy

http://en.wikipedia.org/wiki/Internal_energy

http://en.wikipedia.org/wiki/Dynamic_pressure



http://en.wikipedia.org/wiki/Static_pressure

http://en.wikipedia.org/wiki/Gravitational_potential

http://en.wikipedia.org/wiki/Newton%27s_2nd_law

http://www.engineeringtoolbox.com/bernouilli-equation-d_183.html

http://www.engineeringtoolbox.com/bernouilli-equation-d_183.html



III BLOCK DIAGRAM

A pressure sensor measures pressure,

typically of gases or liquids. Pressure is an

expression of the force required to stop a

fluid from expanding, and is usually stated

in terms of force per unit area. A pressure

sensor usually acts as a transducer; it

generates a signal as a function of the

pressure imposed. Pressure sensors are

used for control and monitoring in

thousands of everyday applications.

Pressure sensors can also be used to

indirectly measure other variables such as

fluid/gas flow, speed, water level, and

altitude

PIC

MICROCONTROLLER

LCD DISPLAY

PRESSURE

SENSOR

(INPUT)

ALARM

PRESSURE

SENSOR

(OUTPUT)

(OUTPUT)

Figure2 Block diagram of pressure sensor

for leakage detection A.PRESSURE SENSING

This is where the measurement of

interest is pressure, expressed as

a force per unit area. This is useful in

weather instrumentation, aircraft,

automobiles, and any other machinery that

has pressure functionality implemented.

B. FLOW SENSING

This is the use of pressure sensors

in conjunction with the venturi effect to

measure flow. Differential pressure is

measured between two segments of a

venturi tube that have a different aperture.

The pressure difference between the two

segments is directly proportional to the

flow rate through the venturi tube. A low

pressure sensor is almost always required

as the pressure difference is relatively

small.

C.LEAK TESTING

A pressure sensor may be used to

sense the decay of pressure due to a system

leak. This is commonly done by either

comparison to a known leak using

differential pressure, or by means of

utilizing the pressure sensor to measure

pressure change over time.

IV EXPERIMENTAL RESULTS

The leakages in boiler rectangular

duct are minimized by using pressure

sensor. The figure below shows the

different level of pressure sensing. An LED

is often small in area (less than 1 mm2) and

integrated optical components may be used

to shape its radiation pattern. LEDs have

many advantages over incandescent light

sources including lower energy

consumption, longer lifetime, improved

physical robustness, smaller size, and faster

switching.

Liquid crystal displays (LCDs) have

materials which combine the properties of

both liquids and crystals. The LCD’s are

lightweight with only a few millimetres

thickness. Since the LCD’s consume less

power, they are compatible with low power

electronic circuits, and can be powered for

long durations.

The Proteus schematic capture

module lies at the heart of the system,

and is far more than just another

schematics package. It combines a

powerful design environment with the

ability to define most aspects of the

drawing appearance. The ISIS editor

consists of three main areas.

i. Editing window.

ii .Object selector and

iii Overview window

http://en.wikipedia.org/wiki/Pressure

http://en.wikipedia.org/wiki/Gas

http://en.wikipedia.org/wiki/Liquids

http://en.wikipedia.org/wiki/Transducer

http://en.wikipedia.org/wiki/Function_(mathematics)

http://en.wikipedia.org/wiki/Pressure

http://en.wikipedia.org/wiki/Force

http://en.wikipedia.org/wiki/Venturi_effect

http://en.wikipedia.org/wiki/Radiation_pattern



In this figure two types of pressure

sensors are used. One is used as input

pressure and another one is used as output

pressure sensor. When the output level is

same as input level LED will glow. The

results were obtained by using LCD

display

In this figure the output pressure sensor

decreases while comparing with input

pressure sensor. Hence LED will turn OFF.

It helps to identify the leakages in boiler

rectangular duct.

IV PERFORMANCE ANALYSIS

The leakages are determined by using

pressure sensor. Different levels are

calculated and displayed by using LCD

display.

S.no

Pressure

sensor

(input)

Pressure

sensor

(output)

LED

ON/OFF

state

1

2.3

2.4

Off

2

2.8

2.8

On

3

2.8

2.6

Off

Table1 Different levels of pressure

sensor

V.CONCLUSION

In this paper, the leakages in boiler

rectangular duct were determined by using

pressure sensor. The leakages are occurred

due to many environmental facts. When the

leakage occurs in boiler duct it will be

detected by sensor. By using pressure

sensor, it would control the pressure level

in boiler rectangular duct. When the

pressure level increases, boiler tube will

brake; the leakages were detected by

pressure sensor and displayed by using

LCD display. The results were obtained by

using proteus 7 simulation software .In

future, an array of Pitot tubes can be used

to indicate fluid flow velocity by

measuring the difference between the static

and dynamic pressures in fluids. Pitot tube

is one of the simplest flow sensors it is

used in a wide range of flow measurement

applications. Temperature sensors are used

to measure different levels of temperature.

Pitot tubes are used to measure air flow in

pipes, ducts, stacks and liquid flow in

pipes.

VI REFERENCES

[1] Aime Lay-Ekuakille, Giuseppe Vendramin,

and Amerigo Trotta,Robust Spectral Leak

Detection of Complex Pipelines Using Filter

Diagonalization Method” ieee sensors

journal, vol. 9, no. 11, November 2009.

[2] B. Widarsson, E. Dotzauer, “Bayesian

network based early-warning for leakage in

Recovery boiler” –ATE (2007).

[3] “Idaho State University’s College of

Technology” Energy Systems Engineering

Technology- module 4 flow measurements.

[4] J. L. Martins de Carvalho, Gerhard Jank,

and J. Milhinhos “An LPV Modelling and

Identification Approach to Leakage

Detection in High Pressure Natural Gas

Transportation Networks” IEEE

transactions on control systems technology,

vol. 19.No. 1, January 2011.

[5] J. P. du Plessis and M. R. Collins “A new

definition for laminar flow entrance lengths

of straight ducts” n&o journal September

1992 .

[6] Kun Wang, Heng Lu, Lei Shu, and Joel J. P.

C. Rodrigues “A Context-Aware System

Architecture for Leak Point Detection in the

Large-Scale Petrochemical Industry” IEEE

Communications Magazine June 2014.

[7] Liansuo An, PengWang, AugustoSarti,

Fabio Antonacci, Jie Shi “Hyperbolic boiler

tube leak location based on quaternary

acoustic array” L. An et al. / Applied

Thermal Engineering 31 (2011).

[8] Nariman Sepehri, and Amin Yazdanpanah

Goharrizi “Internal Leakage Detection in

Hydraulic Actuators Using Empirical Mode



Decomposition and Hilbert Spectrum” ieee

transactions on instrumentation and

measurement, vol. 61, no. 2, February 2012.

[9] S. Shahul Hamid D. Najumnissa Jamal

Murshitha Shajahan “Automatic Detection

and Analysis of Boiler Tube Leakage

System” International Journal of Computer

Applications (0975 – 8887) Volume 84 –

No 16, December 2013.

[10] Sanghyo lee,Am cho Jihoon kim,Changdon

kee “Wind Estimation and Airspeed

Calibration using a UAV with a Single-

Antenna GPS Receiver and Pitot Tube” ieee

transactions on aerospace and electronic

systems vol. 47, no. 1 january 2011.

[11] Tabish Alam, R.P. Saini J.S. Saini “Use of

turbulators for heat transfer augmentation in

an air duct” journal T. Alam et al.

Renewable Energy 62 (2014) 689-715.

.

National Conference on Research Advances in Communication, Computation, Electrical Science

and Structures (NCRACCESS-2015)


GENETIC ALGORITHM BASED POWER SYSTEM STABILISER FOR

SINGLE MACHINE INFINITE BUS SYSTEM

K.Kalaiselvan ME,

Assistant Professor, EEE,

Bharathiyar Institute of Engineering for

Women,

Attur(Tk), Salem(Dt), Tamil Nadu, India.

K.C.Kavitha ME,. Assistant professor ,EEE

Bharathiyar Institute of Engineering for

Women,

Attur(Tk), Salem(Dt), TamilNadu, India.

ABSTRACT: Power system stabilizers are now

routinely used in the industry to damp out power

system oscillations, over a wide range of operating

conditions and disturbances. The principal role of

a power system stabilizer is to increase the

damping of oscillations of generator rotor by

control of its excitation with the help of auxiliary

stabilizer signals. Genetic algorithm is one of the

global search techniques to provide a powerful

tool for optimization problems. In this thesis, the

genetic algorithm optimization technique is

applied to design a robust power system stabilizer

with optimal state feedback control for single

machine infinite bus system. The simulation

results of the MATLAB coding employed show

the effectiveness and robustness of the proposed

controller (GAPSS) and their ability to provide

efficient damping of low frequency oscillations.

Keywords: Genetic Algorithm, Power system

stabilizer, State feedback controller, Optimum

feedback controller.

1. INTRODUCTION

Power system stabilizers (PSS) are added to

excitation systems of the generator to enhance the

damping of electric power systems during low-

frequency oscillations. Several methods are used

in the design of PSSs. Recently, several

researchers taking advantage of optimal control

techniques have used modern control methods.

These methods utilize a state-space representation

of the power system model to calculate a gain

matrix which, when applied as a state feedback

control, will minimize a given prescribed objective

function .In practice, not all of the states are

available for measurement. In the state feedback

method the optimal control law requires the design

of a state observer. This increases the

implementation cost and reduces the reliability of

the control system. There is

another disadvantage of the observer-based control

system. Even a slight variation in the model

parameters from their nominal values may result

in significant degradation of the closed-loop

performance. Hence it is desirable to opt for an

output feedback design. The state output feedback

problem is one of the most investigated problems

in control theory. The power system stabilizers are

added to the power system to enhance the damping

of the electric power system. The design of PSS

can be formulated as a optimal linear regulator

control problem whose solution is a complete state

control scheme. The implementation requires the

design of state estimators that consume large time.

Recently, advanced numerical computation

methods such as Artificial Neural Network

(ANN), Fuzzy Logic Systems (FLS) and Genetic

Algorithms (GA) have been applied to various

power system problems including PSS design.

Genetic algorithms are global search techniques

and provide the solution of optimization problem

by miming the mechanism of natural selection and

genetics. In view of the above, the main thrust of

the research work presented in this thesis is to

design a robust power system stabilizer whose

parameters are tuned through GA.

To deal with problem, control scheme uses only

some desired state variables such torque angle and

speed. The desired objectives in this paper are:

• Variations of the angular frequency ( ) can be

achieved to end value equal to zero in minimum

time.




• Variations of the torque angle ( ) can be

achieved to end value equal to minimum value

in minimum time.

2. STUDY SYSTEM

fig.1.Single line diagram of SMIB

The system studied in this paper is own in fig.1. It

is a machine-infinite-bus power system. The

system study is described by following state space

representation:

Where

xT=[Δω Δδ Δe’q ΔeFD ΔVR ΔVE]

ω : Rotor speed

δ : Torque angle

e’q : Q axis component of voltage behind transient

reactance

VR : Regulator output voltage

VE : Exciter output voltage

u : Supplementary control voltage

Δ denotes deviation from operating point.

Where matrices A and B from [2], as follow:

3. OPTIMAL DESIGN

GAs are search techniques using the

mechanics of natural selection and natural genetics

for efficient global searches [8]. In comparison to

the conventional searching algorithms, GAs has

the following characteristics: (a) GAs work

directly with the discrete points coded by finite

length strings (chromosomes), not the real

parameters themselves;(h) GAs consider a group

of points (called a population size) in the search

space in every iteration, not a single point; (c) GAs

use fitness function information instead of

derivatives or other auxiliary knowledge; and (d)

GAs use probabilistic transition rules instead of

deterministic rules. Generally, a simple GA

consists of the three basic genetic operators: (a)

Reproduction; (h) Crossover; and (c) Mutation.

They are described as follows.

(a). Reproduction:

Reproduction is a process to decide how many

copies of individual strings should be produced in

the mating pool according to their fitness value.

The reproduction operation allows strings with

higher fitness value to have larger number of

copies, and the strings with lower fitness values

have a relatively smaller number of copies or even

none at all. This is an artificial version of natural

selection (strings with higher fitness values will

have more chances to survive).

(b). Crossover:

Crossover is a recombined operator for two

high4tnessstrings (parents) to produce two off

springs by matching their desirable qualities

through a random process. In this paper, the

uniform crossover method is adopted. The

procedure is to select a pair of strings from the

mating pool at random, then, a mark is selected at

random. Finally, two new strings are generated by

swapping all characters correspond to the position

of the mark where the bit is “1”. Although the

crossover is done by random selection, it is not the

same as a random search through the search space.

Since it is based on the reproduction process, it is

an effective means of exchanging information and

combining portions of high fitness solutions.

(c) Mutation:

Mutation is a process to provide an occasional

random alteration of the value at a particular string



ISSN: 2348 - 8379 www.internationaljournalssrg.org Page

10

position. In the case of binary string, this simply

means hanging the slate of a bit from 1 to 0 and

vice versa. In this paper we provide a uniform

mutation method. This method is first to produce a

mask and select a string randomly, then

complement the selected string value correspond

to the position of mask where the bit value is “1”.

Mutation is needed because some digits at

particular position in all strings may be eliminated

during here production and the crossover

operations. So the mutation plays the role of a

safeguard in GAs. It can help GAs avoid the

possibility of mistaking a local optimum for a

global optimum. The GA includes five

fundamental parameters: (a) Population size,

which influences amount of search points in every

generation. The more population size in the GAs

will increase the efficiency of searching, but it will

time consuming; (b) Crossover probability, which

influences the efficiency of exchanging

information. In general, the crossover probability

between 0.6 and I; (c) Mutation probability, which

occur with a small probability in the GAs. In

general, the mutation probability under 0.1. A

large mutation probability in GAs will eliminate

the result of reproduction and crossover, which let

GAs become a random search; (d) Chromosome

length, which influences their solution of the

searching result.

The GAs with

longer

chromosome

length will have

the higher

resolution, but it

will increase the

search space;

(e) Generations,

which

influences the

searching time and searching result. The GAs with

larger search space and less population size, it

needs more generations for a global optimum.

For achieving the above desired objectives, the

system is analyzed under two cases.

1.Open loop system

2.Optimal state feedback control system

4. SYSTEM ANALYSIS

Open Loop System

The Eigen values of the study system

under open loop condition are obtained as follows:

Table 1.1. Eigen values of the open loop system

By analyzing the eigen values, the system is in stable stable

condition. But the response of system obtained is more

oscillatory. Hence the system takes more time to settle down.

Optimal State Feedback Controller

In this part, the genetic algorithm is used

for tuning the two weighted matrices are taken for

optimization. The fitness function used for

optimizing Q and R values in genetic algorithm is

as follows:

f (i)=106

/ (a1* max1 +a2* ax2+a3*tp1+a4*tp2 +a5*

ts1+a6*ts2+a7*Fin1+a8*Fin2)

where ai : system performance factor

max1,max2 : maximum function values

tp1, tp2 : time of peak values

Fin1, Fin2 : end values

As known, optimal controller design is based on

the optimal factor K that is given by

u (t) = -Kx(t) (i) -----(i)

K = R-1

BT

p (ii) ---- (ii)

pA+AT p+Q- p BR

-1 B

T p=0 (iii)

Where A and B are state matrices, Q and R are

weighted matrices and K is the optimal gain.

Equation (iii) represents the Riccati equation. In

this thesis, genetic algorithm is implemented here

to get the optimized value of Q and R. First

optimal feedback values were designed by creating

initial population size with 30 Chromosome.

Te following parameters of genetic algorithm used

are:

Population size =30

Crossover probability =0.8

Mutation probability =0.6

Generation =100

Forecasting step size =[0 20]

-0.263+i10.82

-0.263-i10.82

-8.169+i8.951

-8.169-i8.951

-2.885

-1.624




11

In this thesis, the uniform method for

mutation and crossover is proposed. The desirable

values of controller are obtained. By knowing the

optimized value of Q and R and solving the

equation (iii), the value of p is obtained.

AQ=A-B*K

Then the optimal gain K can be calculated. Then

by substituting the value of K in the following

equation (ii), We get the state matrix of optimal

controller AQ is obtained. Also By using the pole-

placement technique, the desired roots required for

the design of PSS are determined. Then by

applying the genetic algorithm for optimizing the

system. Finally the response of the system is

analyzed based on the settling time.

5. RESULT

System response to step input are shown in

figure.2. for open loop case. Results show

instability of system in normal operation.

Figure 2. Output Response To Step Input For Open

Loop System

Settling time and final value have not been desired

for second input when optimum controller was

used to improvement transient characteristic of

stabilizer. However system stability has been

improved considerably.

Figure 3.Output Response To Step Input For

Optimal Controller

Figure 4. Output response to step input for state

feedback controller

6. CONCLUSION

In this paper, a Genetic Algorithm method is used

to design state and optimum feedback controllers

for improvement PSS transient characteristics. The

results for one-machine system is represented by

Genetic Algorithm as simulation studies and

compared with three control cases content open

loop, optimum state feedback controller. However,

the system is stabilized by optimum feedback

controller but results illustrated undesired settling

time and oscillations of final values as secondary




12

input, slightly. As shown, state feedback controller

lead to suitable operation of power system

stabilizer for studied one-machine system and

represented more proportional results than

optimum feedback controller.

REFERENCES

1. Chian-Chuang Ding, King-Tan lee, Chee-Ming Tsai,

Tsong-Liang Huang.“ Optimal Design for Power

System Dynamic Stabilizer by Grey Prediction PID

Control”, IEEE Trans.Power App. Systems,pp279-

284,May 2002.

2. Demello F.P., P. J. Nolan,. Laskowaki T.F, Undrill, J.M

“Coordinate Application of Stabilizers in Multimachine

Power Systems, ”IEEE Trans.Syst.,Vol.PAS-99,pp.892-

901,Nov 1980.

3. Hui-Mei Wag, Tsong-Liang Huaug, Chi-Ming Tsai,

Che-Wei Liu.“Power System stabilizer design Using

Adaptive Back stepping Controller”. IEEE Trans.Power

App. Systems, pp1027-1030, Sep 2002.

4. Hsu Y.Y., Hsu C.Y.,”Design of a Proportional-Integral

Power system Stabilizer,”IEEE Trans.Power App.

Systems, Vol. PWRS-1, No.2, pp. 46-53,Feb1986.

5. Richard K. Warner, Ali Feliachi,”Application of a

Genetic Algorithm Technique to Control a Simple

Power System”, Proceedings of the 36 Proceedings of

the 36th Conference on Decision & Control, IEEE 1997.

6. Y.S.Zhou and L.Y.Lai, “Optimal Design of Fuzzy

Controllers by Genetic Algorithms” IEEE Trans.on

industry applications, Vol.36, No.1 January/February

2002.

National Conference on Research Advances in Communication, Computation, Electrical Science and

Structures (NCRACCESS-2015)


THREE-LEG VSC AND A T-CONNECTED TRANSFORMER BASED

THREE PHASE FOUR WIRE DSTATCOM FOR DISTRIBUTION SYSTEM

K.C.Kavitha ME,.

Assistant professor ,EEE

Bharathiyar Institute of Engineering for Women,


D.Sangeetha ME,


Bharathiar Institute Of Engineering For Women,


ABSTRACT:-

Three-phase four-wire distribution systems are

used in commercial buildings, office buildings,

hospitals, etc. Most of the loads in these locations

are nonlinear loads and are mostly unbalanced

loads in the distribution system. This creates

excessive neutral current both of fundamental and

harmonic frequency and the neutral conductor gets

overloaded. The voltage regulation is also poor in

the distribution system due to the unplanned

expansion and the installation of different types of

loads in the existing distribution system.

In this thesis, a new three-phase four-wire

distribution static compensator (DSTATCOM)

based on a T-connected transformer and a three-leg

voltage source converter (VSC) is proposed for

power quality improvement. The T-connected

transformer connection mitigates the neutral current

and the three-leg VSC compensates harmonic

current and balances the load. Two single-phase

transformers are connected in T-configuration for

interfacing to a three-phase four-wire power

distribution system. The insulated gate bipolar

transistor (IGBT) based VSC is supported by a

capacitor and is controlled for the required

compensation of the load current. The dc bus

voltage of the VSC is regulated during varying load

conditions. The DSTATCOM is tested for

harmonic elimination, neutral current compensation

along with voltage regulation, and balancing of

linear loads as well as nonlinear loads. The

synchronous reference frame theory is used for the

control of the proposed DSTATCOM The

performance of the three-phase four wire

DSTATCOM is validated using MATLAB

software with its Simulink and power system block

set toolboxes

I INTRODUCTION

Electric power distribution network

becomes more increasingly important and plays an

essential role in power system planning. Three

phase four-wire distribution power system has been

widely used for supplying low-level voltage to

office building, commercial complexes,

manufacturing facilities, etc. The loads connected

to the three-phase four-wire distribution power

system may be either the single-phase or the three-

phase loads. Non-linear loads draw current that are

non-sinusoidal and thus create voltage drops in

distribution conductors that are non-sinusoidal.

Most of these loads have the nonlinear input

characteristic, which creates a problem of high

input current harmonics. The harmonic current will

pollute the power system and result in the problems

such as transformer overheats, rotary machine

vibration, degrading voltage quality, damaging

electric power components, medical facilities

malfunction, etc In order to meet the increasing

reactive power demands reactive power

compensation has been recognized as an efficient

and economic means of increasing power

transmission capability. To complete this challenge,

it requires careful design for power network

planning. There exist many different ways to do so.

However, one might consider an additional device

to be installed somewhere in the network. Such

devices are one of capacitor bank, shunt reactor,

series reactors, automatic voltage regulators and/or

recently developed dynamic voltage restorers,

distribution STATCOM (our focus), or

combination of them.

At present, a wide range of very flexible

controllers, which capitalize on newly available

power electronics components, are emerging for




custom power applications. Among these, the

distribution static compensator and the dynamic

voltage restorer are most effective devices, both of

them based on the VSC principle. A DVR injects a

voltage in series with the system voltage and a D-

STATCOM injects a current into the system to

correct the voltage sag, swell and interruption. The

DSTATCOM has plenty of applications in low

voltage distribution systems aimed to improve the

quality and reliability of the power supplied to the

end-user.it can be used to prevent non-linear loads

from polluting the rest of the distribution system.

The rapid response of the DSTATCOM makes it

possible to provide continuous and dynamic control

of the power supply including voltage and reactive

power compensation, harmonic mitigation and

elimination of voltage sags and swells.

II CONTROL ALGORITHM OF DSTATCOM

There are many control schemes available for

control of shunt active compensators. The control

approaches available for the generation of reference

source currents for the control of VSC of

DSTATCOM for three-phase four-wire system are

instantaneous reactive power theory (IRPT)/PQ

theory, synchronous reference frame theory

(SRFT), power balance theory, instantaneous

symmetrical components based, etc. They are

described as follows:

Synchronous Reference Frame Theory

As the application of ac machines has

continued to increase over this century, new

techniques have been developed to aid in their

analysis. Much of the analysis has been carried out

for the treatment of the well-known induction

machine. The significant breakthrough in the

analysis of three-phase ac machines was the

development of reference frame theory. Using these

techniques, it is possible to transform the phase

variable machine description to another reference

frame. By judicious choice of the reference frame,

it proves possible to simplify considerably the

complexity of the mathematical machine model.

The synchronous reference frame

theory(SRFT) is based on the determination of the

instantaneous active and reactive currents(id and

iq).The SRFT creates a reference frame of

orthogonal axes that rotates at the supply

frequency(d-q system),that is, a synchronous

reference. This synchronism with the supply can be

achieved by a phase locked loop (PLL) connected

to the supply voltages or currents. In some

situations only the supply frequency is necessary

for applying the SRFT, so that, the supply phase is

not needed. In this rotating reference, the

fundamental stator current becomes dc values in the

id-iq currents that can be determined by some kind

of low-pass filter. In order to calculate the id and iq

currents, the invariant power Clarke transformation

is applied to the stator currents, followed by the

Park transformation, so that, the stator currents at

the a-b-c system are transferred to the α-β-0 system

and from the α-β-0 system to the d-q-0 system.

Equations (1) and (2) show the Park and the

invariant power Clarke transformations,

respectively.

(1)

(2)

Where θ is the phase angle of the phase voltage;

and the fundamental frequency unit vectors, sin(θ)

and cos (θ), are determined by the PLL.

The id and iq components can be both divided into

alternating (ac) and constant parts(dc),as shown

below.

(3)

After the Park transformation the

fundamental stator currents becomes the dc parts of

the id and iq currents(idˉ and iqˉ)and all the rest of

the harmonics become the ac parts of them(id ˜and

iq˜) with a frequency offset equal to the supply

frequency. Therefore, eliminating the ac parts of id

and iq, that is, id˜ and iq˜, the fundamental currents

at the d-q-0 system, that is , id˜

and iq˜, will last.

The elimination of ac parts of id and iq can be done

by some kind of low pass filter.

Once the idˉ and iqˉ currents were determined, they

must be transformed to the α-β-0 system by the




inverse invariant power Clarke transformation as in

following equation (4).

(4)

And, so to the a-b-c system by the inverse invariant

power Clarke transformation in equation (5)

(5)

III SYSTEM CONFIGURATION AND

DESIGN

The schematic diagram of three-phase four-

wire compensated system is shown in Figure 4.1.

The compensator and the load are connected at a

point called as point of common coupling (PCC).

The load may be unbalanced and non-linear.

Figure. Schematic diagram of three-phase

four-wire compensated system

COMPENSATOR STRUCTURE AND MODELING

The proposed DSTATCOM consisting of a

three-leg VSC and a T-connected transformer is

shown in Fig. 2, where the T-connected transformer

is responsible for neutral current compensation. The

windings of the T-connected transformer are

designed such that the mmf is balanced properly

in the transformer. The VSC converts the dc

voltage across the storage device into a set of three-

phase ac output voltages. These voltages are in

phase and coupled with the ac system through the

reactance of the coupling transformer. Suitable

adjustment of the phase and magnitude of the D-

STATCOM output voltages allows effective control

of active and reactive power exchanges between the

D-STATCOM and the ac system. Such

configuration allows the device to absorb or

generate controllable active and reactive power.

Figure. Schematic diagram of VSC and T-

transformer Based DSTATCOM in Distribution

System

In the present work neutral clamped converter

topology is used to track the reference currents

using two-level voltage source converter as shown

in Figure4.2. The structure of two-level VSC

consists of six IGBT switches, each with anti-

parallel diodes and a dc storage capacitor.

In two-level inverter six IGBT switches

are used each with the dc storage capacitors are

used for VSC operation, they will discharge in due

course of time, due to switching losses in the




compensator. The dc capacitor connected at the dc

bus of the converter acts as an energy buffer and

establishes a dc voltage for the normal operation of

the DSTATCOM system. If the voltage across any

of these capacitors falls below the peak of system

voltage, then the inverter will not track the

reference currents properly. Therefore for proper

operation of compensator, the total voltage across

the capacitor is to be maintained at the reference

voltage level. The ripple filter at the point of

common coupling (PCC) for reducing the high

frequency ripples. The high frequency ripple

voltage is due to the switching current of the VSC

of the DSTATCOM.

The required compensation to be provided by

the DSTATCOM decides the rating of the VSC

components. The data of DSTATCOM system

considered for analysis is shown in the Appendix.

The T-connected transformer mitigates the neutral

current and the three-leg VSC compensates the

harmonic current and reactive power, and balances

the load. The insulated gate bipolar transistor

(IGBT) based VSC is self-supported with a dc bus

capacitor and is controlled for the required

compensation of the load current. The selection of

interfacing inductor, dc capacitor, and the ripple

filter are given

in the following sections.

Capacitor Voltage

The minimum dc bus voltage of VSC of

DSTATCOM should be greater than twice the peak

of the phase voltage of the system. The dc bus

voltage is calculated as

Vdc =2√2VLL

√3m

(1)

where m is the modulation index and is considered

as 1, and VLLis the ac line output voltage of

DSTATCOM. Thus, Vdc is obtained as 677.69 V for

VLLof 415 V and is selected as 700 V.

DC Bus Capacitor

The value of dc capacitor (Cdc) of VSC of

DSTATCOM depends on the instantaneous energy

available to the DSTATCOM during transients. The

principle of energy conservation is applied as

½ Cdc [(v2

dc)-(V2dc1)]= 3V (a I) t

(2)

where Vdc is the reference dc voltage and Vdc1 is the

minimum voltage level of dc bus, a is the

overloading factor, V is the phase voltage, I is the

phase current, and t is the time by which the dc bus

voltage is to be recovered. Considering the

minimum voltage level of the dc bus, Vdc1 =690 V,

Vdc = 700 V, V = 239.60 V, I = 27.82 A, t = 350 μs,

a = 1.2, the calculated value of Cdc is 2600 μF and

is selected as 3000 μF.

AC Inductor

The selection of the ac inductance (Lf) of

VSC depends on the current ripple icr,p-p switching

frequency fs, dc bus voltage(Vdc), and Lf is given as

Lf=√3mVdc

12a fs icr,p-p(3)

where m is the modulation index and a is the

overload factor. Considering, icr,p-p = 5%, fs= 10

kHz, m = 1, Vdc = 700 V, a = 1.2, the Lf value is

calculated to be 2.44 mH. A round-off value of Lf

of 2.5 mH is selected in this investigation.

Ripple Filter

A low-pass first-order filter tuned at half

the switching frequency is used to filter the high-

frequency noise from the voltage at the PCC.

Considering a low impedance of 8.1 Ω for the

harmonic voltage at a frequency of 5 kHz, the

ripple filter capacitor is designed as Cf= 5 μF. A

series resistance (Rf) of 5 Ω is included in series

with the capacitor (Cf). The impedance is found to

be 637 Ω at fundamental frequency, which is

sufficiently large, and hence, the ripple filter draws

negligible fundamental current.

Design of the T-connected Transformer

The T-connected transformer is used in the

three-phase distribution system for different

applications. But the application of T-connected

transformer for neutral current compensation is

demonstrated for the first time. Moreover, the T-

connected transformer is suitably designed for

magnetic motive force (mmf) balance. The T-

connected transformer mitigates the neutral current




and the three-leg VSC compensates the harmonic

current and reactive power, and balances the load.

Figure.Winding diagram of T-Connected

Transformer

The T-connected windings of the

transformer not only provide a path for the zero-

sequence fundamental current and harmonic

currents but also offer a path for the neutral current

when connected in shunt at point of common

coupling(PCC). Under unbalanced load, the zero-

sequence load-neutral current divides equally into

three currents and takes a path through the T-

connected windings of the transformer. The current

rating of the windings is decided by the required

neutral current compensation. The voltages across

each winding are designed as shown shortly.

The phasor diagram gives the following relations

to find the turn’s ratio of windings. If Va1 and Vb1

are the voltages across each winding and Vais the

resultant voltage, then

Va1 = K1Va (4)

Vb1 = K2Va (5)

where K1and K2are the fractions of winding in the

phases. Considering |Va | = |Vb | = Vand Va1 =

Vacos 30◦, Vb1 = Vasin 30◦, then from (4) and (5),

we gets, K1 = 0.866 and K2 = 0.5.

The line voltage is

Vca= 415 V

Va= Vb= Vc=415/√3= 239.60 V

(6)

Va1 = 207.49 V, Vb1 = 119.80 V.

(7)

Hence, two single-phase transformers of rating 5

kVA, 240 V/120 V and 5 kVA, 208 V/208 V

are selected.

SYNCHRONOUS REFERENCE FRAME

IMPLEMENTATION

The control approaches available for the

generation of reference source currents for the

control of VSC of DSTATCOM for three-phase

four-wire system are instantaneous reactive power

theory (IRPT), synchronous reference frame theory

(SRFT),unity power factor (UPF) based,

instantaneous symmetrical components based, etc.

The SRFT is used in this thesis for the control of

the DSTATCOM.

Figure .Control Block of DSTATCOM in

Distribution System

A block diagram of the control scheme is

shown in Fig. 4.4. The load currents (iLa, iLb, iLc),

the PCC voltages (vSa, vSb, vSc), and dc bus voltage

(vdc) of DSTATCOM are sensed as feedback

signals. The load currents from the a–b–c frame are

first converted to the α–β–o frame and then to the

d–q–o frame using

(8)

where cos θ and sin θ are obtained using a three-

phase phase locked loop (PLL). A PLL signal is

obtained from terminal voltages for generation of

fundamental unit vectors for conversion of sensed

currents to the d–q–o reference frame. The SRF

controller extracts dc quantities by a low-pass filter,

and hence, the non-dc quantities (harmonics) are

separated from the reference signal. The d-axis and




q-axis currents consist of fundamental and

harmonic components as

(9)

(10)

CONTROL OF DSTATCOM

UPF Operation of DSTATCOM

The control strategy for reactive power

compensation for UPF operation considers that the

source must deliver the mean value of the direct-

axis component of the load current along with the

active power component current for maintaining the

dc bus and meeting the losses (iloss) in

DSTATCOM. The output of the proportional-

integral (PI) controller at the dc bus voltage of

DSTATCOM is considered as the current (iloss) for

meeting its losses

iloss (n) = iloss (n−1) + Kpd (vdc(n)− vdc(n−1)) + Kidvdc(n)

(11)

where vdc(n) = v*dc− vdc(n) is the error between the

reference(v*dc) and sensed (vdc) dc voltages at the

nth sampling instant. Kpdand Kidare the proportional

and integral gains of the dc bus voltage PI

controller.

The reference source current is therefore

i*d= id dc + iloss(12)

The reference source current must be in phase with

the voltage at the PCC but with no zero-sequence

component. It is therefore obtained by the

following reverse Park’s transformation with i*das

in (12) and i*qand i*0 as zero

(13)

Zero-Voltage Regulation (ZVR) Operation of

DSTATCOM

The compensating strategy for ZVR

operation considers that the source must deliver the

same direct-axis component i*d, as mentioned in

(12) along with the sum of quadrature-axis current

(iq dc) and the component obtained from the PI

controller (iqr) used for regulating the voltage

at PCC. The amplitude of ac terminal voltage (VS)

at the PCC is controlled to its reference voltage (V

* S) using the PI controller. The output of PI

controller is considered as the reactive component

of current (iqr) for zero-voltage regulation of ac

voltage at PCC. The amplitude of ac voltage (VS) at

PCC is calculated from the ac voltages (vsa, vsb, vsc)

as

VS = (2/3)1/2

(Vsa2+Vsb

2+Vsc

2)

1/2

(14)

Then, a PI controller is used to regulate this voltage

to a reference value as

iqr (n) = iqr(n−1) + Kpq(vtc(n)− vtc(n−1)) + Kiq vtc(n)

where vte(n) = V * S − VS(n) denotes the error between

reference(V * S ) and actual (VS(n) ) terminal voltage

amplitudes at the nth sampling instant. Kpqand

Kiqare the proportional and integral gains of the dc

bus voltage PI controller. The reference source

quadrature - axis current is

i*q= iqdc + iqr.

The reference source current is obtained by reverse

Park’s transformation using (13) with i*d as in (12)

and i*q as in (16) and i*0 as zero

Current-Controlled pulse width modulation

(PWM) Generator

In a current controller, the sensed and

reference source currents are compared and a

proportional controller is used for amplifying

current error in each phase before comparing with a

triangular carrier signal to generate the gating

signals for six IGBT switches of VSC of

DSTATCOM.

SIMULATION AND RESULTS

SIMULINK

SIMULINK is a companion program to

MATLAB. It is an interactive system for simulating

non linear dynamic systems. It is graphical mouse

driven program that allows modeling of system by

drawing a block diagram on the screen and

manipulating it dynamically. It can work with




linear, non-linear, continuous and discrete time,

multivariable and multi rate system.

Block sets are add-ins to simulink that

provides additional libraries of blocks for

specialized applications like communications,

signal processing and power systems.

SIMULATION OF DSTATCOM SYSTEM

The three-leg VSC and the T-transformer

based DSTATCOM connected to a three-phase

four-wire system is modeled and simulated using

the MATLAB with its Simulink and Power System

Blockset toolboxes. The system data are given in

Appendix. The MATLAB based model of the

three-phase four-wire DSTATCOM is developed.

The control algorithm for the DSTATCOM

is also modeled in MATLAB. The reference source

currents are derived from the sensed PCC voltages

(vsa, vsb, vsc), load currents (iLa, iLb, iLc) and the dc

bus voltage of DSTATCOM (vdc). A pulse width

modulated (PWM) current controller is used over

the reference and sensed source currents to generate

the gating signals for the IGBTs of the VSC of the

DSTATCOM.

The performance of the three-phase four-

wire DSTATCOM is demonstrated for power factor

correction and voltage regulation along with

harmonic reduction, load balancing and neutral

current compensation. The developed model is

analyzed under varying loads and the results are

shown below.

OUTPUT WAVEFORMS OF DSTATCOM

WITH LINEAR LOAD UNDER ZVR

OPERATION

5.5. OUTPUT WAVEFORMS OF DSTATCOM

WITH NON-LINEAR LOAD UNDER ZVR

OPERATION




INFERENCES

The source neutral current is observed as nearly

zero, and this verifies the proper compensation. It is

also observed that the dc bus voltage of

DSTATCOM is able to maintain close to the

reference value under all disturbances. The

amplitude of PCC voltage is maintained at the

reference value under various load disturbances,

which shows the ZVR mode of operation of

DSTATCOM. The dc bus voltage of DSTATCOM

is maintained at nearly its reference value under all

load disturbances

5.7 OUTPUT WAVEFORMS OF DSTATCOM

WITH LINEAR LOAD UNDER UPF

OPERATION

5.9. OUTPUT WAVEFORMS OF DSTATCOM

WITH NON-LINEAR LOAD UNDER UPF

OPERATION




CONCLUSION

The performance of a new topology of three-

phase four-wire DSTATCOM consisting of three-

leg VSC with a T-connected transformer has been

demonstrated for neutral current compensation

harmonic elimination, and load balancing. The T-

connected transformer has mitigated the source-

neutral current. The voltage regulation and power

factor correction modes of operation of the

DSTATCOM have been observed and are as

expected. The dc bus voltage of the DSTATCOM

has been regulated to the reference dc bus voltage

under all varying loads. The performance of

DSTATCOM is verified under linear and non-

linear load conditions. The Simulink result shows

that the DSTATCOM compensates the harmonic

current and balances the load.

National Conference On Research Advances In Communication, Computation, Electrical Science And Structures (NCRACCESS-2015)


ANALYSIS OF GENETIC ALGORITHM TO SOLVE ECONOMIC

LOAD DISPATCH PROBLEM IN THE POWER SYSTEM

R.KAYALVIZHI

PG Scholar,

M.E-Power Systems Engineering

Muthayammal College of Engineering, India

ABSTRACT- In this paper, an efficient and practical

real-coded genetic algorithm (GAs) has been proposed

for solving the economic load dispatch (ELD) problem.

The objective is to minimize the total generation fuel

cost and keep the power flows within the security limits.

For each problem of optimization in genetic algorithms

(GAs) there are a large number of possible encodings.

The efficiency of the GAs is increased as there is no

need to convert chromosomes to the binary type, less

memory is required. There is no loss in precision. The

proposed technique improves the quality of the solution

and speed of convergence of the algorithm. The Coding

are written and executed the values are plotted in graph

for different values of MW loading.

Index term- Economic Load Dispatch, Fuel cost, Genetic

Algorithm, MW loading

I.INTRODUCTION

Improve the reliability and efficiency of power systems,

new communication technologies, and distributed energy

Sources, and demand response programs have been intro-

duced. These efforts are mainly motivated by the increasing

Costs of fossil fuels, environmental changes, and energy

security concerns coupled with investments in wind and

solar generation to replace conventional CO -emitting

energy sources. The increased flexibility of the power sys-

tem results in a higher level of complexity for economic

dispatch (ED) problems.

Real-time dispatch is, in general, computed in two stag-

es. In the first stage, a unit commitment (UC) problem is

solved to select generating units to meet the expected load

during each hour. In the second stage, an ED problem is

solved to compute the power outputs of the committed

units for meeting the load. This ED decision takes place

minutes to hours ahead of the time of implementation.

Recent work has explored UC for planning purposes to

accommodate generation/load forecasting uncertainties,

mostly based on standard commercial solvers. However,

the location-based marginal prices (LMPs) are found by

solving the corresponding ED problem with fixed unit

commitment decisions.

II. SCOPE

The economic dispatch (ED) problem is one of the most

important operational functions of the modern clay energy

management system. The purpose of the ED is to find the

optimum generation among the existing units, such that the

total generation cost is minimized while simultaneously

satisfying the power balance equations and various other

constraints in the system. The literature of the ED problem

and its solution methods are surveyed. However, it is rea-

lized that the conventional techniques become very compli-

cated when dealing with increasingly complex dispatch

problems, and are further limited by their lack of robustness

and efficiency in a number of practical applications.

Recently, a global optimization technique known as GAs

which is a kind of the probabilistic heuristic algorithm has

been studied to solve the power optimization problems. The

GAs may find the several sub-optimum solutions within a

realistic computation time. The efficiency and the robust-

ness of the proposed GAs are demonstrated by test func-

tions. Then the GAs with simulated non uniform arithmetic

crossover, elitism and a non uniform mutation are applied

to ED problem. III.CHALLENGES IN MEETING POWER DEMAND

The main objective oof Economic load dispatch of

electric power generation is to schedule the commited ge-

nerating units output so as to meet the load demand at min-

imum operating cost, while satisfying all the unit and the

system equality and inequality constraints

For the purpose of economic dispatch studies,online

generators are represented by functions that relates their

production cost to their power output.Quadratic cost func-

tions are used to model generator in order to simplify the

mathematical formulation of the problem and to allow

many of the convensional optimization technique to be



used. IV. OPTIMIZATION TECHNIQUE FOR THE ECO-NOMIC DISPATCH IN POWER SYSTEM OPERA-TION

The optimal economic operation of their electric net-

works while considering the challenges of increasing fuel

costs and increasing demand for electricity. The dynamic

economic dispatch (DED) occupies important place in a

power system’s operation and control. It aims to determine

the optimal power outputs of on-line generating units in

order to meet the load demand and reducing the fuel cost.

The nonlinear and non convex characteristics are more

common in the DED problem. Therefore, obtaining a op-

timal solution presents a challenge. In the proposed system,

genetic algorithm(GA) – a recently introduced population-

based technique – is utilized to solve the DED problem. We

demonstrate 3 units and 6 units generating system for simu-

late the maximize power output and minimize the fuel cost.

4.1.1 NEED FOR ECONOMIC LOAD DISPATCH

1.Planning for Tomorrow’s Dispatch

i. Scheduling generating units for each hour of the

next day’s dispatch

a. Based on forecast load for the next day

b. Select generating units to be running and

available for dispatch the next day (operat-

ing day)

ii. Recognize each generating unit’s operating limit,

including its:

iii. Ramp rate (how quickly the generator’s output can

be changed)

iv. Maximum and minimum generation levels

v. Minimum amount of time the generator must run

a. Minimum amount of time the generator

must stay off once turned off

2. Recognize generating unit characteristics, including:

i. Cost of generating,

ii. Its variable operating costs (fuel and non-fuel),

iii. Variable cost of environmental compliance,

iv. Start-up costs,

3. Reliability Assessment.

4. Dispatching the Power System Today.

4.1.2 OVER VIEW OF ECONOMIC LOAD DIS-PAATCH

Economic dispatch is the short-term determination of

the optimal output of a number of electrical genera-

tion facilities, to meet the system load, at the lowest possi-

ble cost, while serving power to the public in a robust and

reliable manner. The Economic Dispatch Problem is solved

by specialized computer software which should honor the

operational and system constraints of the available re-

sources and corresponding transmission capabilities. This is

defined as "the operation of generation facilities to produce

energy at the lowest cost to reliably serve consumers, re-

cognizing any operational limits of generation and trans-

mission facilities.

4.1.3 Solution methods for Economic load dispatch

Some of the algorithms are given below, they are, 1. Artificial neural networks 2. Genetic algorithms 3. Evolutionary algorithms 4. Particle swarm optimization 5. Ant colony optimization 6. Fuzzy logic 7. Other biological systems. 4.1.5 Algorithm Used In Proposed System

The algorithm used here is the Genetic algorithm(GA)

is well-known stochastic methods of global optimization

based on the evolution theory of Darwin. They have suc-

cessfully been applied in different real-world applica-

tions.With this proposed technique we will reduce the fuel

cost and losses.

V. GENETIC ALGORITHM

5.1.1 Introduction

GA was originally developed for solving unconstrained

problems. Recently, many variants of GAs have been de-

veloped for solving constrained nonlinear programming.

Most of these methods were based on penalty formulations

that transform (1) into an unconstrained function Fm(PG,rk)

(6), consisting of a sum of the objective and the constraints

weighted by penalties, and use GAs to minimize

Fm(PG,rk). GAs, unlike strict mathematical methods, have

the apparent ability to adapt to non linear ties and disconti-

nuities commonly found in power systems.

The basic idea behind GAs is to mathematically imitate the

evolution process of nature. The algorithms are based on

the evaluation of a set of solutions, called population. The

population is treated with genetic operations. At the itera-

http://en.wikipedia.org/wiki/Electricity_generation

http://en.wikipedia.org/wiki/Electricity_generation



tion i the population Xi consist of a number of N individu-

al’s xj, that is, solutions, where N is called a population

size. The population is initialized by randomly generated

individuals.

The individuals can be encoded using either binary or real

numbers. We use the latter because of their popularity. Each

individual xj = (x1, …,xn) is a vector of variables. Each

variable is a real number. The suitability of an individual is

determined by the value of the objective function, to be

called a fitness function.

A new population is generated by the genetic operations

selection, crossover and mutation. Parents are chosen by

selection and new off springs are produced with crossover

and mutation. All these operations include randomness. The

success of the optimization process is improved by elitism

where the best individuals of the old population are copied

as such to the next population.

5.1.2 Optimization with GA

For each problem of optimization in GAs there are a

large number of possible encodings. Although binary repre-

sentation is usually applied to power optimization prob-

lems, in this letter we use a GA switch is a modified GAs

employing real valued vectors for representation of the

chromosomes. The use of real valued representation in the

GAs has a number of advantages in numerical function op-

timization over binary encoding.

The efficiency of the GAs is increased as there is no need

to convert chromosomes to the binary type, less memory is

required, there is no loss in precision by discretization to

binary or other values, and there is greater freedom to use

different genetic operators.

For the real valued representation, the k-th

Chromosome Ck can be defined as follows

Ck=[Pk1, Pk2 ,…,Pkn] k=1,2,…,popsize

Where pop size means population size and Pki is the gener-

ation power of the i-th unit at k-th chromosome. Reproduc-

tion involves the creation of new offspring from the mating

of two selected parents or mating pairs. It is thought that

the crossover operator is mainly responsible for the global

search property of the GA. A non-uniform arithmetic cros-

sover operator was introduced into the GAs.

5.1.3 Economic load dispatch with GA

The economic dispatch problem, which is used to mi-

nimize the cost of production of real power, can generally

be stated as follows:

Economic load dispatch problem is allocating loads to

plants for minimum cost while meeting the constraints. It

is formulated as an optimization problem of minimizing the

total fuel cost of all committed plant while meeting the de-

mand and losses .The variants of the problems are numer-

ous which model the objective and the constraints in differ-

ent ways.

The basic economic dispatch problem can described ma-

thematically as a minimization of problem of minimizing

the total fuel cost of all committed plants subject to the

constraints. n

i

iPMinimize1

i )(F

…….(1)

)( ii PF is the fuel cost equation of the ‘i’th plant. It is the

variation of fuel cost ($ or Rs) with generated power

(MW).Normally it is expressed as continuous quadratic

equation. maxmin2

,)( iiiiiiiiiij PPPcPbPaPF …..(2)

The total generation should meet the total demand and

transmission loss. The transmission loss can be deter-

mined form either Bmn coefficients or power flow.

l

n

i

i PDP1 ………(3)

n

i

n

j

jiijl PPBP

……..(4)

GAs is a probabilistic search technique, which generates

the initial parent vectors distributed uniformly in intervals

within the limits and obtains global optimum solution over

number of iterations. The implementation of GAs is given

below. The initial population is generated after satisfying

the equation. The elements of parent vectors ( PGi ) are the

real power outputs of generating units distributed uniformly

between their minimum and maximum limits.

The fitness function is used to transform the cost function

value into a measure of relative fitness. The fitness function

is given in equation.

1. Select a reference plant. For better convergence

chose a plant which has maximum capacity and range.

In this program It is considered as plant 1. The refer-

ence plant allocation is fixed by the equations



(A3&A4).

2. Convert the constrained optimization problem as an

unconstrained problem by penalty function method.

n

i

n

j

jiij

n

i

i

n

i

i PPBDPabsP

Minimize

1 111

i )(*1000)(F

……..(5)

3. The allocation minimum fuel cost and transmission

losses can be determined

Table 5.1.3 Genetic algorithm optimization

POWER DEMAND

UNIT1 OPTIMIZED POWER

500

354.3868

600

274.4185

700

320.9227

800

355.078

900

380.207

1000

406.8564

Fig 5.1.3 Graph for Genetic algorithm optimiza-

tion

5.1.4 Algorithm for GA

Step 1

Input the value of load demand, power demand and the

values of cost coefficients, ai, bi, ci where i=1, 2…n.

Step 2

Input the values of Bmn coefficients in the non linear equa-

tion

Step 3

Update the loss coefficients and find the total demand and

the incremental fuel cost.

Step 4

Assume Pi = 0 for i = 1, 2...n

Step 5

Fix the limit for generating plant and solve equation itera-

tively for Pi’s.

Step 6

Check if test unit 1 converges to load demand at its limit.

Similarly check the conversion of the other plants.

Step 7

Check if power balance equation is satisfied and find fuel

cost and emission when all the 6 test units are converged.

The above said steps are explained in following flowchart

5.1.5 Flowchart for GA

StartA

initialization

Evaluate Pl and update PD

Substitute Bmn coefficients in the non linear equations&

find incremental fuel cost

Determine optimal Power limit

of plant 1,2...6.

Are plant1,2,3,4 load de-

mand converged to power

limit?

Determine optimal Power limit

of plant 1,2...6.

Substitute Bmn coefficients in the non linear equations&

find incremental fuel cost



NO

A YES

YES

NO

NO

NON

YES

Fig 5.1.5 Flow chart for ELD using GA

VI SIMULATION AND RESULT

6.1 PROBLEM FORMULATION

The ED problem may be expressed by minimizing the

fuel cost of generator units under constraints. Depending on

load variations, the output of generators has to be changed

to meet the balance between loads and generation of a

power system. The power system model consists of n gene-

rating units already connected to the system.

The ED problem can be expressed as:

Where,

ai, bi and ci are the cost coefficients of the it generator.

NG is the number of generators including the slack bus.

PGi is the real power output of the i-th generator (MW).

Fi(PGi) is the operating cost of unit i ( $/h). 6.2 CONSTRAINTS

Subjects to the following constraints,

GAs is a general stochastic optimization algorithm that was

originally developed for solving unconstrained problems.

By applying an exterior penalty function we transform a

constrained non-linear ED problem into an unconstrained

A

A

Are plants 5,6 load

demand converged to

power limit?

Find Fcost and Ecost

Stop



problem

6.6.2 Input datas

1. Loss co-efficients.

2. Data matrix (Fuel cost co-efficient(ai, bi and c i) and

plant limits).

ai = measure of losses in the system.

bi = represents the fuel cost.

ci = includes salary and wages, interests and deprecia-

tion.

3. Load demand(D).

6.6.3 Simulation results

F = 1.0813e+04 P1 = 386.3605 116.8379 201.3102 64.3634 99.1567 50.4517 Pl = 18.4805

VIII. CONCLUSION

In this paper, an approach based on a genetic algorithm

has been successfully presented and applied to the genera-

tion cost in electric power network to obtain the optimum

solution of economic dispatch (ED). Operators are used in

LAGRANGIAN to generate a set of solutions for this prob-

lem. LAGRANGIAN method is most useful for large pow-

er systems, it LAGRANGIAN ve well results and it is

much faster and more effective than iterative method. Me-

thods are compared for solving an economic dispatch prob-

lem with two generators. Test results have shown GA algo-

rithm can provide highly optimal solutions and reduces the

computation time than those with the iterative method. An

advantage of the GA solution is the flexibility it provides in

modeling both time dependent and coupling constants [10].

Another advantage of the GA approach is the ease with

which it can handle arbitrary kinds of constraints and ob-

jectives.



REFERENCE

[1]. AlKalaani,Y., F.E. Villaseca, and F.Jr. Renovich,(1996) Fuel-

Constrained Unit Commitment, IEEE Transactions on

Power Systems, Vol. PWRS-11, No. 2, pp. 1059-1066.

[2]. Ana V. and el al,(2003) Using GRASP to Solve the Unit

Commitment Problem, Annals of Operations Research

120(1); 117-132.

[3]. Beltran, C.,(2002) Unit Commitment by Augmented Lagra-

gian Relaxation: Testing Two Decomposition Approaches,

Journal of Optimization Theory and Applications, 112(2):

295-314.

[4]. C.L. TSENG, C.L.,(2000) Solving the Unit Commitment

Problem by a Unit De commitment Method, Journal of opti-

mization Theory and Applications, 105(3): 707-730.

[5]. Hobbs, B.F and et al.(2001), The Next Generation of Electric

Power Unit Commitment Models, Kluwer: Academic Pub-

lishers.

[6]. Jorge V.,(2002) A Seed Memetic Algorithm for Large Unit

Commitment Problems, Journal of Heuristics 8(2):173-195.

[7]. Padhy, N.P.,Unit Commitment(2004)- A Biblography Survey,

IEEE Trans. Power Systems, Vol. 19, no. 2, pp. 1196-1205.

[8]. Robert N., and Werner R.,(2002) A two-Stage Planning Mod-

el for Power Scheduling in a Hydro-Thermal System Under

Uncertainty, Optimization and Engineering 3(4): 355-378.

[9]. Wood, W.G.,(1982) Spinning Reserve Constrained Static

Dynamic Dispatch, IEEE Trans. Power Systems, Vol.101,

pp.381-388.

[10]. Wood and Wollenberg,(1996) Power Generation, Operation

and Control, John Wiley and Sons.




PERFORMANCE ANALYSIS OF PHOTOVOLTAIC PUMPING

SYSTEM FOR BLDC MOTOR USING FUZZY LOGIC

CONTROLLER

J.P. Srividhya M.E., (PhD)., Department of EEE,

Assistant professor,

SKP Engineering College,

Thiruvannamalai, India

ABSTRACT:-A new converter for photovoltaic water

pumping system without the use of batteries was

introduced. The converter is designed to drive a bldc

motor directly coupled from Photovoltaic source. The

use of Bldc motor presents a better solution to the

commercial dc motor water pumping system. The

advanced (or) developed system is based on dc-dc

converter of sepic converter. We use an intelligent

control method (P&O) for searching the maximum

power point (MPP). This method uses a fuzzy logic

controller applied to a drive a DC–DC converter to an

optimal operating point using PV Panel’s measured

variables.

Key-Words: - Pumping system, photovoltaic,

MPPT, BLDC, Fuzzy logic controller

1. INTRODUCTION:-

One of the major concerns in the power sector

is the day-to-day increasing power demand,but the

unavailability of enough resources to meet the power

demand using the conventional energy sources.

Demand has increased for renewable sources of

energy to be utilized along with conventional

systems to meet the energy demand. Renewable

sources like wind energy and solar energy are the

prime energy sources which are being utilized in this

regard. The continuous use of fossil fuels has caused

the fossil fuel deposit to be reduced and has

drastically affected the Environment depleting the

biosphere and cumulatively adding to global

warming. Solar energy is abundantly available that

has made it possible to harvest it and utilize it

properly. Solar energy can be a standalone

generating unit or can be a grid connected generating

unit depending on the availability of a Grid nearby.

Thus it can be used to power rural areas where the

availability of grids is very low. Another advantage

of using solar energy is the portable operation

whenever wherever necessary.

E.Suresh,

Department of PSE,

PG Student,

SKP Engineering College,

Thiruvannamalai, India

In order to tackle the present energy crisis one has to

develop an efficient manner in which power has to be

extracted from the incoming solar radiation. The

power conversion mechanisms have been greatly

reduced in size in the past few years. The

development in power electronics and material

science has helped engineers to come up very small

but powerful systems to withstand the high power

Demand. But the disadvantage of these systems is the

increased power density. The trend has set in for the

use of multi-input converter units that can effectively

handle the voltage fluctuations. But due to high

production cost and the low efficiency of these

systems they can hardly compete in the competitive

markets as a prime power generation source. The

constant increase in the development of the solar

cells.

Manufacturing technology would definitely make

the use of these technologies possible on a wider

basis than What the scenario is presently. The use of

the newest power control mechanisms called the

Maximum Power Point Tracking (MPPT) algorithms

has led to the increase in the efficiency of operation

of the solar modules and thus is effective in the field

of utilization of renewable sources of energy. In

order to extract the maximum power of the PV array,

the classical implementation of the maximum power

point tracking (MPPT) in stand-alone systems is

generally accomplished by the series connection of a

dc–dc converter between the PV array and the load

or the energy storage element. The operation

principle, theoretical analysis, design methodology,

and experimental results of a laboratory prototype of

the MPPT system are presented in this paper.

Permanent magnet DC motors use a

mechanical commutator and brushes to achieve the

commutation. However, BLDC motors adopt Hall

Effect sensors in place of mechanical commutator

and brushes. The stators of BLDC motors are the

coils, and the rotors are the permanent magnets the

stators develop the magnetic fields to make the rotor

rotating. Hall Effect sensors detect the rotor position

as the commutating signals. Therefore, the BLDC

motors use permanent magnets instead of coils in the

armature and so do not need the brushes. As the rotor




position is detected by incremental encoder then the

Hall Effect sensors used to detect rotor position for

BLDC motor

The conversion of solar light into electrical energy

represents one of the most Promising and challenging

energy technologies, in continuous development,

being clean, silent and reliable, with very low

maintenance costs and minimal ecological impact.

Solar energy is free, practically inexhaustible, and

involves no Polluting residues or greenhouse gas

emissions.

The conversion principle of solar light into

electricity, called Photo- Voltaic or PV Conversion,

is not very new, but the efficiency improvement of

the PV conversion equipment is still one of top

priorities for many academic and/or industrial

research groups all over the world.

In this paper, an intelligent control technique using

fuzzy logic Controller is associated to an MPPT

controller in order to improve energy conversion

efficiency of a PV standalone water pumping system

2 .DESIGN OF PUMPING SYSTEM

The following figure describes elements constituting

the water pumping system figure

2.1 PV array

Fig.2.1 Block diagram of the BLDC

water pumping system

This is the most important element since it provides

the electric power needed from the water pumping

System; we chose the PV panel Majority of

commercial PV cells are fabricated from Silicon. A

PV cell is essentially a large diode that produces a

voltage when exposed to incident light. It may be

considered to be a light-emitting diode ―run

backward the analogy is similar to a heat engine and

a refrigerator.

The PV generator is a non-linear device and is

usually described by its equivalent circuit and the I-V

characteristics; the electrical model of a solar cell is

composed of a diode, two resistances and a current

generator. The relationship between the voltage V

(V) and the current I (A) am given by

Where IL, I0 and I are the photocurrent, the inverse

Saturation current and the operating current, RS and

RP are series and parallel resistances, respectively,

which depend on the incident solar radiation and the

cell temperature A=KT/q is the diode quality factor.

K and q are Boltzmann constant and electronic

charge respectively.

The current and the voltage parameters of the PV

Generator is: Ipv =I and Vpv = nsNsV, where ns, Ns

are the numbers of series cells in the panel and of the

series panels in the generator. The PV generator

consists of solar cells connected in series and parallel

fashion to provide the desired voltage and current

required by the load.

2.2 PHOTOVOLTAIC MODULE

The voltage generated by a single solar cell

is very low, around 0.5V. So, a number of solar cells

are connected in both series and parallel connections

to achieve the desired output. In case of partial

shading, diodes may be needed to avoid reverse

current in the array. Good ventilation behind the

solar panels are provided to avoid the possibility of

less efficiency at high temperatures.

Fig.2.2 Simplified circuit diagram of a solar PV

cell




Shows typical I-V characteristics for increasing

insolation levels of the used PV array. The short

circuit current varies in proportion to the insolation

level, while the open circuit voltage is approximately

constant. Consequently, the extracted electric power

will increase accordingly. Each curve has a

maximum power point, which is the optimal

operating point for an efficient use of the solar array

3.DC-DC CONVERTER

3.1 SINGLE-ENDED PRIMARY-INDUCTOR

CONVERTER

Single-Ended Primary-Inductor Converter (SEPIC) is a type of allowing the electrical potential

(voltage) at its output to be greater than, less than, or

equal to that at its input; the output of the SEPIC is

controlled by the duty cycle of the control transistor.

A SEPIC is similar to a traditional buck-boost

converter, but has advantages of having non-inverted

output (the output has the same voltage polarity as

the input), using a series capacitor to couple energy

from the input to the output (and thus can respond

more gracefully to a short-circuit output), and being

capable of true shutdown: when the switch is turned

off, its output drops to 0 V, following a fairly hefty

transient dump of charge.

SEPICs are useful in applications in which a battery

voltage can be above and below that of the

regulator's intended output. For example, a single

lithium ion battery typically discharges from 4.2

volts to 3 volts; if other components require 3.3 volts,

then the SEPIC would be effective.

Fig.3.1 Schematic of SEPIC

4. CONTROLLER

4.1 MPPT METHODS

There are a large number of algorithms that are able

to track MPPs. Some of them are simple, such as

those based on voltage and current feedback, and

some are more complicated. There are many MPPT

methods available in the literature; the most widely-

used techniques are described in the following such

as

Perturbation And Observation (P&O)

Incremental Conductance (Inc Cond)

Method.

Constant Voltage Method

Parasitic Capacitance Method

Fuzzy Logic Controller and etc.

4.2 FUZZY LOGIC CONTROL SYSTEM

In contrast to conventional control techniques, fuzzy

logic control (FLC) is best utilized in complex ill-

defined processes that can be controlled by a skilled

human operator without much knowledge of their

underlying dynamics.

The basic idea behind FLC is to incorporate the

"expert experience" of a human operator in the

design of the controller in controlling a process

whose input – output relationship is described by

collection of fuzzy control rules (e.g., IF-THEN

rules) involving linguistic variables rather than a

complicated dynamic model. The utilization of

linguistic variables, fuzzy control rules, and

approximate reasoning provides a means to

incorporate human expert experience in designing

the controller.

FLC is strongly based on the concepts of fuzzy sets,

linguistic variables and approximate reasoning

introduced in the previous chapters. This chapter will

introduce the basic architecture and functions of

fuzzy logic controller, and some practical application

http://en.wikipedia.org/wiki/Voltage

http://en.wikipedia.org/wiki/Duty_cycle

http://en.wikipedia.org/wiki/Lithium_ion_battery

http://en.wikipedia.org/wiki/File:SEPIC_Schematic.gif




examples. A typical architecture of FLC is shown

below, which comprises of four principal comprises:

a fuzzifier, a fuzzy rule base, inference engine, and a

defuzzifier.

Fig.4.1 A Fuzzy Logic System

If the output from the defuzzifier is not a control

action for a plant, then the system is fuzzy logic

decision system. The fuzzifier has the effect of

transforming crisp measured data (e.g. speed is10

mph) into suitable linguistic values (i.e. fuzzy sets,

for example, speed is too slow).The fuzzy rule base

stores the empirical knowledge of the operation of

the process of the domain experts. The inference

engine is the kernel of a FLC, and it has the

capability of simulating human decision making by

performing approximate reasoning to achieve a

desired control strategy. The defuzzifier is utilized to

yield a non fuzzy decision or control action from an

inferred fuzzy control action by the inference engine.

4.3 STRUCTURE OF A FUZZY

CONTROLLER

4.4 OPERATION OF FUZZY

CONTROLLER

5. VOLTAGE SOURCE INVERTER

The main objective of static power

converters is to produce an ac output waveform from

a dc power supply. These are the types of waveforms

required in adjustable speed drives (ASDs),

uninterruptible power supplies (UPS), static var

compensators, active filters, flexible ac transmission

systems (FACTS), and voltage compensators, which

are only a few applications. For sinusoidal ac

outputs, the magnitude, frequency, and phase should

be controllable. According to the type of ac output

waveform, these topologies can be considered as

voltage source inverters (VSIs), where the

independently controlled ac output is a voltage

waveform. These structures are the most widely used

because they naturally behave as voltage sources as

required by many industrial applications, such as

adjustable speed drives (ASDs), which are the most

popular application of inverters. Similarly, these

topologies can be found as current source inverters

(CSIs), where the independently controlled ac output

is a current waveform. These structures are still

widely used in medium-voltage industrial

applications, where high-quality voltage waveforms

are required. Static power converters, specifically

inverters, are constructed from power switches and

the ac output waveforms are therefore made up of

discrete values. This leads to the generation of

waveforms that feature fast transitions rather than

smooth ones. For instance, the ac output voltage

produced by the VSI of a standard ASD is a three-

level waveform. Although this waveform is not

sinusoidal as expected, its fundamental component

behaves as such. This behaviour should be ensured




by a modulating technique that controls the amount

of time and the sequence sused to switch the power

valves on and off. The modulating techniques most

used are the carrier-based technique (e.g., sinusoidal

pulse width modulation, SPWM), the space-vector

(SV) technique, and the selective-harmonic-

elimination (SHE) technique.

Fig.5.1 Three-phase VSI topology.

The inverter provides three-phase system

voltages variable in amplitude and frequency to

operate with variable loads and frequency

(from0.1 up to 1 time the rated frequency) The

current is modulated sinusoidally to obtain a

high efficiency. The pulse frequency is maximal

2kHz.The phase voltage can be expressed as

follows

6. BLDC MOTOR

Brushless DC (BLDC) motors are synchronous

motors with permanent magnets on the rotor and

armature windings on the stator. Hence, from a

construction point of view, they are the inside-out

version of DC motors, which have permanent

magnets or field windings on the stator and armature

windings on the rotor. A typical BLDC motor with

12 stator slots and four poles on the rotor the most

obvious advantage of the brushless configuration is

the removal of the brushes, which eliminates brush

maintenance and the sparking associated with them.

Having the armature windings on the stator helps the

conduction of heat from the windings. Because there

are no windings on the rotor, electrical losses in the

rotor are minimal. The BLDC motor compares

favorably with induction motors in the fractional

horsepower range. The former will have better

efficiency and better power factor and, therefore, a

greater output power for the same frame, because the

field excitation is contributed by the permanent

magnets and does not have to be supplied by the

armature current. These advantages of the BLDC

motor come at the expense of increased complexity

in the electronic controller and the need for shaft

position sensing. Permanent magnet (PM) excitation

is more viable in smaller motors, usually below 20

kW. In larger motors, the cost and weight of the

magnets become

Fig.6.1 Three-phase BLDC motor

6.2 MACHINE CONSTRUCTION

BLDC motors are predominantly surface-magnet

machines with wide magnet pole-arcs and

concentrated stator windings. The design is based on

a square waveform distribution of the air-gap flux

density waveform as well as the winding density of

the stator phases in order to match the operational

characteristics of the self-controlled inverter

7. CENTRIFUGAL PUMP MODEL

The centrifugal pump applies a load torque

proportional to the square of the rotational speed of

the motor

The performances (Q ', H' and P ') are given in terms

of the speed using the following relationships




Fig.7.1 Liquid flow path inside a centrifugal pump

8. RESULTS AND DISCUSSION

Show that output voltage DC/DC sepic

converter we note that the system follows the

variations of irradiation

Fig.8.1 Simulation Model

8.2 STATOR CURRENT VS TIMES

Fig.8.2 Shows That the Bldc Motor Stator

Current D (A)

8.3 STATOR VOLTAGE VS TIMES

Fig.8.3Shows That the Bldc Motor Stator voltage

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16-10

-5

0

5

10

15

TIME (S)

CU

RR

EN

T (

A)

<Stator current is_d (A)>

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16-5

0

5

10

15

20

25

30

35

Time (S)

voltage (

V)




8.4 STATOR CURRENT VS. TIMES

Fig.8.4 Shows That the Bldc Motor Stator three

phase current

9. CONCLUSION

This project presents the simulation work of a

Photovoltaic array feeding a BLDC motor using

pumping system. SEPIC converter and voltage

source inverter were used as interface between PV

module and the BLDC motor. Fuzzy logic controller

algorithm was used to track the maximum power

from PV module. The simulation works of these

circuits were carried out in the MATLAB/simulink

software. The SEPIC converter gives the constant

output voltage using the FLC and the BLDC motor

can running at constant speed and sliding mode

technique used to estimate the rotor position.

10. REFERENCES

[1] Xin Wang and Aiguo Patrick Hu “An Improved

Maximum Power Point Tracking Algorithm For

Photovoltaic Systems”, Australasian Universities Power

Engineering Conference , Brisbane, Australia, 2004.

[2] F. M. González-Longatt: Model of photovoltaic module

in Matlab. In 2do congreso iberoamericano de estudiantes

de ingenierıacute;a eléctrica, electrónica y computación,

iicibelec, 2005, pp. 1–5.

[3] K.Rajashekara, A.Kawamura, et al, “Sensorless Control

of AC Motor Drivers,” IEEE press, 1996. January 2005.

[4]N.Femia,G.Petron,G.Spagnuolo,andM.Vitelli,

“Optimization of perturb and observe maximum power

point tracking method,” IEEE Trans. Power Electron., vol.

20, no. 4, pp. 963–973, Jul. 2005.

[5] J. A. Abu-Qahouq, H. Mao, H. J. Al-Atrash, and I.

Batarseh, “Maximum efficiency point tracking (MEPT)

method and digital dead time control implementation,”

IEEE Trans. Power Electron., vol. 21, no. 5, pp. 1273–

1281, Sep. 2006.

[6] V. Salas, E. Olias, A. Barrado and A. Lazaro, „Review

of the Maximum Power Point Tracking Algorithms for

Stand-Alone Photovoltaic Systems‟, Solar Energy Materials

& Solar Cells, Vol. 90, N°11, pp. 1555 – 1578,Jul. 2006.

[7] W. Xiao, W. G. Dunford, P. R. Palmer, and A. Capel :

Regulation of photovoltaic voltage. In IEEE Trans. Ind.

Electron., vol. 54, no. 3, pp. 1365– 1374, Jun. 2007.

[8] R. B. Darla, “Development of Maximum Power Point

Tracker for PV Panels Using SEPIC Converter” in

proceeding on Telecommunications Energy conference,

INTELEC 2007, pp 650 655.

[9]Chee Wei Tan, Tim C. Green and Carlos A. Hernandez-

Aramburo“Analysis of Perturb and Observe Maximum

Power Point Tracking Algorithm for Photovoltaic

Applications”, 2nd IEEE International Conference on

Power and Energy (PECon 08), December 1-3, 2008, Johor

Baharu, Malaysia.

[10]Z. Yan, L. Fei, Y. Jinjun, and D. Shanxu: Study on

realizing MPPT by improved incremental conductance method with variable step-size. In Proc. IEEE ICIEA, Jun.

2008, pp. 547–550.

[11] M. G. Villalva, J.R. Gazoli, E. R. Filho,

“Comprehensive Approach to Modeling and Simulation of

PV Arrays”, IEEE Transactions on Power Electronics, Vo.

24, No. 5, pp 1198-1208, May 2009.

[12]E. E. Jimenez-Toribio, A. A. Labour-Castro and

F.M.RoDriguez, “Sensorless Controll of SEPIC and Cuk

Converters for DC Motors using Solor Panels” in

proceeding on Electrical Machines and Drives conference,

IEMDC-09, 2009, pp 1503-1510.

[13] M. Salhi, and R. El-Bachtiri, Maximum Power Point

Tracking controller for PV systems using a PI regulator

with boost DC/DC converter, ICGST-ACSE journal, vol. 8,

issue III, pp. 21-27, 2009.

[14] M. Salhi, and R. El-Bachtiri, A Maximum Power

Point Tracking Photovoltaic System using a Proportional

Integral Regulator, Science Academy Transactions on

Renewable Energy Systems Engineering and Technology,

vol. 1, pp. 37-44, June 2011.

[15] M. Salhi, A. Saadi, and R. El-Bachtiri,

"Dimensionnement d‟un convertisseur boost DC/DC pour

la poursuite du point de puissance maximale d‟un système

photovoltaïque", 2ème congrès de l‟Association Marocaine

de Thermique (AMT), 18-19 Avril, Casablanca, Morocco,

2012.

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16-15

-10

-5

0

5

10

15

Time(S)

CU

RR

EN

T (A

)

National Conference on Research Advances in Communication, Computation, Electrical Science and Structures

(NCRACCESS-2015)


FOUR AREA INTERCONNECTED SYSTEM ON LOAD FREQUENCY

CONTROL USING FIREFLY ALGORITHM

S. Priyadharshini ME.,


Bharathiyar Institute of Engineering for women


K.Kalaiselvan ME.,

Assistant Professor, EEE,

Bharathiyar Institute of Engineering for Women,


ABSTRACT: The paper deals with optimal tuning of a

PID controller for a load frequency control of four

areas Power system using Firefly algorithm. The

proposed approach has superior feature, including

easy implementation, stable convergence

characteristics and very good computational

performances efficiency. The main objective is to

obtain a stable, robust and controlled system by

tuning the PID controller using Firefly algorithm.

The interconnected four area LFC system is modeled

and simulated using MATLAB-SIMLINK

environment and the PID control parameters are

tuned based on FA algorithm. By comparison with

the conventional technique, the effectiveness of the

anticipated scheme is confirmed. Hence the results

establishes that tuning the PID controller using the

firefly optimisation technique gives less over shoot,

system is less sluggish. Optimization technique finds

the best parameters for controller and designed

controller are an optimal controller. The simulated

results are obtained for different load configurations

of the Firefly algorithm based controller and this

indicate that the better control performance in terms

of overshoot and settling time can be achieved by

choosing PID among the other considered classical

controllers.

Keywords: LFC, PID, Power System Control, Firefly

algorithm.

I. INTRODUCTION

In a power system, the generating electric power

unit must satisfy the load demand to all consumers in the

system with desired qualities. The main objective of

power system control is to maintain the continuous

balance between electrical generation and varying load

demand and the associated system losses while system

frequency and voltage level are maintained constant. The

load variations in the power system affect the quality of

power. If the power demand is more than the generated

power, system frequency will decrease and if the power

demand is lesser than the generated power, system

frequency will increase affecting the real power of the

system [1]. Hence the balance of the power system gets

disturbed. To supply the load demand without giving

much constrain to a single system and to improve the

reliability, power systems are interconnected and power

is exchanged between the systems over the tie-lines by

which they are connected. An approach on the

“Evolutionary Computation based Four-Area Automatic

Generation Control in Restructured Environment”[4].

load frequency control (LFC) is a very important issue

in power system operation and control for supplying

sufficient and both good quality and reliable power.

A new methodological approach on “Optimizing

power flow of AC–DC power systems using artificial

bee colony algorithm”[16]. PID controller improves the

transient response of a system by reducing the

overshoot, and by shortening the settling time of a

system. Although new methods are proposed for tuning

the PID controller, their usage is limited due to

complexities arising at the time of implementation.

Since, Firefly Optimization algorithm is an optimization

method that finds the best parameters for controller in

the uncertainty area of controller parameters and

obtained controller is an optimal controller. The

objective of this study is to investigate the load

frequency control and inter area tie-power control

problem for a four-area power system taking into

consideration the uncertainties in the parameters of

system [1]. An optimal control scheme based firefly

Algorithm (FA) method is used for tuning the

parameters of this PID controller. The proposed

controller is simulated for a four-area power system. To

show effectiveness of proposed method and also

compare the performance of these four controllers,

several changes in demand of the four areas

simultaneously are applied. Simulation results indicate

that Firefly algorithm based controllers guarantee the

good performance under various load

conditions.

II.FOUR AREA POWER SYSTEM:

Power systems have variable and complicated

characteristics and comprise different control parts and

also many of the parts are nonlinear.These parts are

connected to each other by tie lines and need


(NCRACCESS-2015)


controllability of frequency and power flow.

Interconnected multiple-area power systems can be

depicted by using circles. A simplified four area

interconnected power system used in this study is shown

in Fig. 1.

Fig 1.Simplified interconnected power system diagram.

In an interconnected power system, different

areas are connected with each other tie-lines. When the

frequencies in two areas are different, a power exchange

occurs through the tie-line that connected the two areas,

where ∆Ptieij is tie-line exchange power between areas i

and j, and Tij is the tie-line synchronizing torque

coefficient between area i and j as shown in fig.2.it can

see that the tie-line power error is the integral of the

frequency difference between the two areas.

Fig 2.Block diagram of the tie line

Where, Δfi & Δfj: are the two areas interconnected.

Tij: the tie line synchronizing torque coefficient

ΔPtie : tie line power exchange between areas I and j.

Laplace transformation of the tie line is given by,

ΔPtie12(s)=2ΠT0/s(Δf1(s)-Δf2(s))

(1) The system state-space model can be represented as

ẋ =Ax+Bu

y=Cx

(2)

Where, system matrix A, input matrix B, state matrix x,

control matrix u and output matrix C

u=[u1 u2 u3 u4]T

y = [y1 y2 y3 y4] T=[Δf1 Δf2 Δf3 Δf4]

T

x = [Δf1 ΔPT1 ΔPG1 ΔPC1 ΔPtie1

Δf2 ΔPT2 ΔPG2 ΔPC2 ΔPtie2

Δf3 ΔPT3 ΔPG3 ΔPC3 ΔPtie3

Δf4 ΔPT4 ΔPG4 ΔPC4 ΔPtie4] T

(3)

For the four area considered in this study, the

conventional integral controller was replaced by a PID

controller with the following structure.

K(s) = KP +KI / S+ KDS

Where KP is the proportional gain, KI is the integral

gain, and KD is the differential gain, respectively. The

PID controllers in both areas are considered to be

identical.

Fig. 3. Block diagram of a four-area power system.

The control signal for PID controller can be given in the

following equation.

Ui(s) = -k(s)*ACEi(s)

(4)

Now a performance index can be defined by adding the

sum of squares of cumulative errors in ACE, hence

based on area control error a performance index J can be

defined as:

J=

0

2^)( dtACE i

(5)


(NCRACCESS-2015)


Based on this performance index J optimization problem

can be stated as: Minimize J subjected to:

KP min

≤ KP ≤ KPmax

KI

min≤ KI ≤ KI

max

KD

min≤ KD≤ KD

max

(6)

IV METHODOLOGY OF FIREFLY ALGORITHM:

Nature-inspired methodologies are among the

most powerful algorithms for optimization problems.

Firefly algorithm is a novel nature-inspired algorithm

inspired by social behavior of fireflies. Fireflies are one

of the most special, captivating and fascinating creature

in the nature. There are about two thousand firefly

species, and most fireflies produce short and rhythmic

flashes. The main part of a firefly‟s flash is to act as a

signal system to attract

other fireflies. Firefly-inspired algorithms use the

following three idealized rules:

(1) All fireflies are unisex which means that they are

attracted to other fireflies regardless of their sex;

(2) the degree of the attractiveness of a firefly is

proportion to its brightness, thus for any two flashing

fireflies, the less brighter one will move towards the

brighter one and the more brightness means the less

distance between two fireflies. If there is no brighter one

than a particular firefly, it will move randomly;

(3) the brightness of a firefly is determined by the value

of the objective function. For a maximization

problem, the brightness of each firefly is proportional to

the value of the objective function. In case of

minimization problem, brightness of each firefly is

inversely proportional to the value of the objective

function. Based on the effectiveness of the firefly

algorithm in optimizing continuous problems, it is

predictable that this algorithm can also be modified to

solve discrete optimization problems in an effective

manner. In general, firefly algorithm incorporates three

important strategies which are given as follows.

A.ATTRACTIVENESS

In the firefly algorithm, the main form of

attractiveness function β(r) can be any monotonically

decreasing functions such as the following generalized

form:

β(ɼ )=β0e-γrm

, m ≥ 1

(7)

where r is the distance between two fireflies, β0 is the

initial attractiveness of firefly and γ is a absorption

coefficient.

B. DISTANCE BETWEEN FIREFLIES:

The distance between any two fireflies p and q at

positions xp and xq respectively, can be defined as a

Cartesian or Euclidean distance as follows:

rpq=||Xp-Xq||=√∑k-1d(Xp,s-Xq.s)

2

(8)

where xp,s is the sth component of the spatial coordinate

of the pth firefly and d is the total number of dimensions.

Also q ε {1,2,. . . ,Fn} is randomly chosen index.

Although q is determined randomly, it

has to be different from p. Here Fn is the number of

fireflies. For other applications such as scheduling, the

distance can be any of the suitable forms, not necessarily

the Cartesian distance.

C. MOVEMENT OF FIREFLY:

The movement of a firefly p, when attracted to

another more attractive (brighter) firefly q, is determined

by

X1=Xp+β(ɼ )˟(Xp-Xs)+α(rand-1\2 )

(9)

The third term introduces randomization with „α‟ being

the randomization parameter and „„rand‟‟ is a random

number generated uniformly distributed

between 0 and 1.

Fig 4 .Firefly behaviour


(NCRACCESS-2015)


Fig 5.Flowchart of firefly algorithm

V.ALGORITHM

In the firefly algorithm, the optimization process

depends on the brightness of the fireflies and the

movement of fireflies towards their brighter

counterparts. Every firefly is attracted to the other

depending on brightness because the fireflies are all

unisexual according to the first assumption about

artificial fireflies. The following section describes the

pseudo code.

Step 1: Define an initialize objective function f(x), x =

(x1,..xd)

Step 2: Generate initial population of fireflies xi (i = 1, 2,

..., n)

Step 3: Determine light intensity for xi by calculating

f(xi)

Step 4: Define light absorption coefficient γ

While t < Maximum Generation

Make a copy of the generated firefly

population for move function

For i = 1 : n all n fireflies

For j = 1 : i all n fireflies

If (Ij > Ii ),

Move fireflies i and j according to

attractiveness

Evaluating new solutions and updating

light intensity for next iteration

End if End for j

End for i

Sorting the fireflies to find the present best

End while Begin post process on best results obtained

During the iterative process, the brightness of one firefly

is compared with the others in the swarm and the

difference in the brightness triggers the movement. The

distance travelled depends on the attractiveness between

the fireflies. During the iterative process the best

solution thus far is continuously updated and the process

goes on until certain stopping conditions are satisfied.

After the iterative process comes to a halt the best

solution of the evaluation is determined and the post

process is initiated to obtain the results. The flowchart

diagram is shown in Figure 5.

VI. RESULTS AND SIMULATIONS:

In this section different comparative cases are

examined to show the effectiveness of the proposed FA

method for optimizing Controller parameters. table 1

gives the optimum values of the overshoot, Table 2 gives

the values of the under shoot and the table 3 gives the

values of the settling time. The simulation results are

shown in Figs. 6,7 in

this study.

Table 1: comparison of PID and optimisation of the four

area power system for overshoot

AREA OVER SHOOT (HZ)

PID FIREFLY

Area 1 0.005 0.0055

Area 2 0.013 0.005

Area 3 0.013 0.003

Area 4 0.006 0.01


(NCRACCESS-2015)


Table 2: comparison of PID and optimisation of the

four area power system for undershoot

Table 3: comparison of PID and optimisation of the

four area power system for Settling time

The four area system can be simulated using the

MATLAB-SIMULINK environment. The simulation

results shown in Figs. 4 and 5 gives the frequency

response and step load change using PID controller and

the firefly algorithm based optimisation technique. Thus

the simulation results shows the following,

Fig 6: Frequency response and step load change of

four area system with PID controller

Fig 7.Frequency response and step load change of

four area system using optimisation technique

CONCLUSION:

Firefly algorithm is proposed in this four area power

system to tune the parameters of PID for LFC.

Optimisation -PID controller is suggested to generate

good quality and reliable electric energy. Simulation

results emphasis that the designed FA tuning PID

controller is robust in its operation and gives a good

damping performance both for frequency and tie line

power deviation compared to conventional controller.

AREA

UNDER

SHOOT (HZ)

PID FIREFLY

Area 1 -0.021 -0.012

Area 2 -0.023 -0.012

Area 3 -0.023 -0.013

Area 4 -0.032 -0.031

AREA

SETTLING TIME (SEC)

PID FIREFLY

Area 1 45 36

Area 2 66 50

Area 3 63 46

Area 4 52 37


(NCRACCESS-2015)


Also, these controllers have a simple architecture and the

potentiality of implementation in real time environment.

REFERENCES 1. M.Peer Mohamed, E.A.Mohamed Ali, I.Bala Kumar “BFOA based

tuning of PID controller for a load frequency control in four area

power system” 2012 International Journal of Communications and

Engineering .

2.Lei Xiao Lianfang Kong “A New Model Predictive Control

Scheme-Based Load-Frequency Control”2007 IEEE International

Conference on Control and Automation.

3.Tomonobu Senjyu, Motoki Tokudome, Akie Uehara, Toshiaki

Kaneko, Atsushi Yona, Hideomi Sekine, and Chul-Hwan Kim “New

Control Methodology of Wind Farm using Short-Term Ahead Wind

Speed Prediction for Load Frequency Control of Power System”

IEEE International Conference on Power and Energy 2008.

4. Praghnesh Bhatt, S.P. Ghoshal, Ranjit Roy “Evolutionary

Computation based Four-Area Automatic Generation Control in

Restructured Environment”International Conference on Power

Systems 2009.

5.Xiangjie Liu, Xiaolei Zhan and Dianwei Qian “Load Frequency

Control considering Generation Rate Constraints” Intelligent Control

and Automation 2010.

6.Kre simir Vrdoljak, Nedjeljko Peri´c and Dino ˇSepac “Optimal

Distribution of Load-Frequency Control Signal to Hydro Power

Plants” IEEE 2010.

7.H.Bevrani, F.Habibi, P.Babahajyani, M. Watanabe, Y. Mitani

“Intelligent Frequency Control in an AC Microgrid Online PSO-

Based Fuzzy Tuning Approach” IEEE transactions on smart grid, vol.

3 dec 2012.

8.A.-K. Marten,D. Westermann “Load Frequency Control in an

interconnected power system with an embedded HVDC Grid” IEEE

2012.

9.Manisha Sharma, Laxmi Shrivastava, Manjree Pandit “Corrective

Action Planning For Power System Load Frequency Control”

International Conference on Power, Energy and Control (ICPEC)

2013.

10.K.Suresh,N.Kumarappan“Hybrid improved binary particle swarm

optimization approach for generation maintenance scheduling

problem “Swarm and Evolutionary Computation 9 (2013).

11.Tsai-Hsiang Chen,Nien-Che Yang “Loop frame of reference

based three-phase power flow for unbalanced radial distribution

systems” Electric Power Systems Research (2010).

12. Yusuf Oysala, A.Serdar Yilmaz, Etem Koklukaya “A Dynamic

wavelet network based adaptive load frequency control in power

systems” Electrical Power and Energy Systems 27 (2005).

13. Ahmed M. Kassem”Neural predictive controller of a two-area

load frequency control for interconnected power system” electrical

engineering 2010.

14.Han Huang,Hu Qin,Zhifeng Hao, Andrew Lim ”Example-based

learning particle swarm optimization for continuous optimization”

Information Sciences 182 (2012).

15.Hadi Besharat,Seyed Abbas Taher “Congestion management by

determining optimal location of TCSC in deregulated power systems”

Electrical Power and Energy Systems 30 (2008).

16.Ulas Kılıc,Kurs_at Ayan “Optimizing power flow of AC–DC

power systems using artificial bee colony algorithm” Electrical

Power and Energy System mar 12.

17.Hany E.Farag, E.F.El-Saadany, Ramadan El Shatshat,Aboelsood

Zidan “A generalized power flow analysis for distribution systems

with high penetration of distributed generation” Electric Power

Systems Research 81 (2011).

18.S.Baghya shree, S. Somaselvakumar “Genetic algorithm based

decentralized load frequency control in deregulated environment”

journal on Advanced trends in computer science and engineering,

vol.2 , no.2, pages : 128-133 (2013).

19. kallol das,priyanath das,sharmistha Sharma“Load frequency

control using classical controller in an isolated single area and two

area reheat thermal power system” Emerging technology and

advanced engineering mar 2012.

20.Alwadie.A “Stabilizing Load Frequency of a Single Area Power

System With Uncertain Parameters Through a Genetically Tuned PID

Controller” Engineering & Computer Science Vol:12.

APPENDIX

AREA 1,2,3&4

Tp1=20sec, Kp1=120, TT1=0.3sec, TG1=0.08sec,

R1=2.4

T12=T13=T14=T21=T23=T31=T32=T41=0.545

T24=T34=T42=T43=0,BS1=BS2=BS3=BS4=0.425:

a12=a41=a23=a31=-1




Step-Down Converter with Efficient ZCS Operation with Load

Variation

[1]K.KALAISELVAN,

[2] S.SHANMUGAPRIYA, M.SWATHI, K.THULASIMANI

[1]Assistant Professor, [2] UG Student, Department of Electrical and Electronics Engineering,

Bharathiyar Institute of Engineering for Women

ABSTRACT— A step-down converter is presented.

It is composed of an auxiliary switch, a diode, and

a coupled winding to the buck inductor in the

conventional buck converter. By transferring the

buck-inductor current to the coupled winding in a

very short period, the negatively built-up leakage

inductor current of the buck winding guarantees

the zero-current switching (ZCS) operation of the

buck switch in all load conditions. Furthermore,

since the negatively built-up leakage inductor

current is minimized after the zero current of the

buck switch is achieved, the unnecessary current

build-up and the conduction loss are minimized.

Therefore, efficient ZCS operation with load

variation is achieved. The operation principle, ZCS

analysis, design, and experimental results of the

proposed converter are presented.

Contents — Buck converter, power conversion,

power electronics, zero-current switching (ZCS).

I. INTRODUCTION

THE STEP-DOWN power-conversion

technique is widely used in power sources for

microprocessors, battery chargers, LED drivers, solar-

power regulators, and so on. For these applications,

high power density, high efficiency, and low noise are

the driving force in power converter research. High

power density can be achieved by increasing

switching frequency since magnetic and capacitive

elements can be designed in small sizes in high

frequencies. However, increasing switching frequency

greatly increases switching power dissipation due to

the hard switching of the power switch in

conventional converters. Moreover, this increases

switching noise. Therefore, to achieve high power

density with high efficiency and low noise, the soft-

switching technique is a prerequisite in modern

switching converters. The choice of the soft-switching

technique depends on the type of switching device,

and the zero-current switching (ZCS) technique is

more preferable in MOSFET switches since it

eliminates the capacitive loss of a power switch

during the turn-on transition. Many types of ZCS

converters have been presented. The resonant type of

ZCS technique is presented. These converters well

utilized the parasitic components of the power switch

with additional resonant components. However,

higher voltage and current stresses are common in

these converters. The active resonant tank is used in to

achieve the ZCS of the power switch. However, this

converter induces conduction loss in the added

inductor and capacitor. ZCS is achieved in by

resonating the current between the interleaved

inductors and the parasitic capacitors of the switches.

However, conduction loss in the auxiliary inductor

occurs all the time, which reduces the efficiency of the

converter. In the auxiliary active ZCS technique is

used. However, since the reduced current is delivered

to output during the on time of the auxiliary switch,

this converter increases the dc value of the buck

inductor and therefore increases its conduction loss. In

the ZCS buck converter with a coupled inductor is

presented. The characteristics of the coupled-inductor

converter are also presented. A simple ZCS buck

converter with a coupled inductor is presented in with

small additional components. However, a large

current is built up in the coupled winding in light-load

conditions and therefore greatly increases conduction

loss. In addition, less power is transferred to the

output during the switch-off period, which increases

the dc value of the buck inductor current. A ZCS

bidirectional converter is presented in by applying two

auxiliary switches and a coupled inductor. However,

additional diodes are necessary to implement

unidirectional auxiliary switches, thereby increasing

component counts. The other ZCS converters

presented in have their own good characteristics and


(NCRACCESS-2015)


drawbacks as mentioned earlier such as the large

number of components, increased device stress,

circulating current, complex structure, and so on.

A new buck converter with efficient ZCS

operation with load variation is presented. The target

application of the proposed converter is in solar-

power regulator modules. The solar-power regulator is

a module that converts solar to electrical energy to

power satellite buses. The proposed converter is

composed of an auxiliary switch, a diode, and a

coupled winding in the conventional buck inductor.

During a very short turn-on period of the auxiliary

switch, the buck inductor current is transferred to the

coupled winding and the leakage inductor current is

built up negatively. It guarantees the ZCS of the

switch in all load conditions through the negative

leakage inductor current. Furthermore, since the

negatively built-up leakage inductor current is

minimized after the zero current of the buck switch is

achieved, the unnecessary current build-up and

conduction loss are minimized as the load goes down,

which indicates the efficient ZCS operation with load

variation. The operation principle, analysis, design

example, and experimental results of the proposed

converter are presented in this paper.

II. OPERATIONAL PRINCIPLE

The circuit of the proposed converter is based

on the conventional buck converter. However, to

achieve the

ZCS’operation

of the power switch during the turn-on transition, the

proposed converter uses an additional switch Sx, a

diode Dx, and a coupled winding on the original buck

inductor, as shown in Fig. 1. The coupled winding has

to have a fewer number of turns compared with the

buck winding for proper operation. Since the rms

current of the coupled winding is small, the coupled

winding requires a small winding area. Therefore, the

buck core of the proposed converter is not much

larger than that of the conventional one. In addition,

the transferred coupled-winding current is not

circulating but delivering to output. Therefore, the

proposed converter does not greatly increase the

magnetics size of the conventional buck inductor. The

current stresses of the additional switch Sx and diode

Dx are also small. The basic idea of the proposed

converter is simple. When switch Sx is turned on, the

buck-inductor current is transferred to the coupled

winding for a short period. Then, after the leakage

inductor current iLr reaches zero, it flows in the

negative direction and to the discharge output

capacitor of switch Sb for ZCS operation.

Fig. 2 shows the gating pulses for the

switches and key operation waveforms of the

proposed converter in a steady state. One switching

cycle is divided into six modes, and their operational

stages are shown in Fig. 3. In order to simplify the

analysis of the steady-state operation, all parasitic

components except for those specified in Fig. 1 are

neglected. It is assumed that the output capacitors of

switch Sb and diode Db have the same capacitance as

Cs for simple analysis. Moreover, the output current

Io, output voltage Vo, and input voltage Vin are

assumed constant during the switching cycle.

Mode 1 (t0−t1): Mode 1 begins when the

leakage inductor current iLr reaches the buck-inductor

current iLb. Since buck switch Sb is in the ON state,

the difference between the input voltage Vin and

output voltage Vo is applied in buck inductor Lb with

the assumption of Lb _ Lr. Then, the coupled-winding

voltage vx, switch voltage vSx, and diode voltage vDx

are

given by

vx =n(Vin − Vo) (1)

vSx =(1 − n)(Vin − Vo) (2)

vDx =Vo + n(Vin − Vo). (3)

Since n < 1 and Vin > Vo, vSx and vDx are positive

and the

output diode of switch Sx and diode Dx remains in the

OFF

state. This mode ends when switch Sb is turned off.

Mode 2 (t1−t2): In Mode 2, diode Db is in the

ON state and the buck-inductor voltage vLb = −Vo.

Then, the coupled winding voltage vx, switch voltage

vSx, and diode voltage vDx are given by

vx = − nVo (4)


(NCRACCESS-2015)


vSx =Vin − (1 − n)Vo (5)

vDx =(1 − n)Vo. (6)

Since n < 1 and Vin > Vo, vSx and vDx are positive

and the

output diode of switch Sx and diode Dx remain in the

OFF state. This mode ends when switch Sx is turned

on.

Mode 3 (t2−t3): When switch Sx is turned on, the

coupled winding voltage vx becomes Vin − Vo. Then,

the voltages of the buck and leakage inductors are

given by

vLb =(Vin − Vo)/n (7)

vLr = −1/n (Vin − Vo) + Vo) (8)

Since turn ratio n is a little smaller than unity, the

leakage

inductor voltage vLr has a large negative value, as

shown in

Then, the leakage inductor current iLr decreases

rapidly down to zero since leakage inductor Lr has a

very small value. The time interval of this mode is

short and given by

The current difference between iLb and iLr is

transferred

to the coupled winding and flows through switch Sx

and the

output capacitor, as shown in Fig. 3(c). As shown by

the

powering current iPo in Fig. 2, the transferred current

is not

circulating but powering to output. Therefore, it does

not induce additional conduction losses and does not

increase the dc value of the buck-inductor current.

The voltage of diode Dx and switch Sb is Vin during

this mode. This mode ends when the leakage inductor

current iLr reaches zero.

Mode 4 (t3−t4): Mode 4 has a very short period

compared

with Mode 3. After the leakage inductor current iLr

reaches

zero, it flows in the negative direction and to the

charge and discharge output capacitors of diode Db

and the output capacitor of switch Sb, respectively, in

a resonant manner, as shown in Fig. 3(d). Since switch

Sx is still in the ON state, the voltage across the buck

inductor remains the same as . Then, the voltage of the

leakage inductor is given by

where vSb(t) varies from Vin to the zero current

during this

mode. The voltage of switch vSb(t) and the current of

the

leakage inductor iLr(t) is given by

As shown in the leakage inductor voltage vLr in Fig. 2

and

the voltage across the leakage inductor is always

negative during t3−t4, which forces the leakage

inductor current to flow in the negative direction

always in this mode. Therefore, the ZCS of switch Sb

is always achieved only if switch Sx is not turned off

before vSb reaches zero. Since the leakage inductor Lr

and output capacitor Cs have very small values and

the time interval within which switch voltage vSb

decreases from Vin to zero is very short, switch Sx is

never turned off before vSb reaches zero. Therefore,

the ZCS of switch Sb is already guaranteed. The time

period of this mode is given by


(NCRACCESS-2015)


Mode 5 (t4−t5): Mode 5 begins when vSb(t) reaches

zero

current. When vSb reaches zero current, the output

diode of

switch Sb is turned on and maintained in its ON state

during

this mode since the voltage of the leakage inductor

vLr remains

negative as

which maintains that the leakage inductor current still

flows in the negative direction, as shown in Fig. 3(e).

Since turn ratio n is slightly less than unity in the

voltage across the leakage inductor vLr has a very

small negative value and the leakage inductor current

iLr increases negatively in a very slow slope, as

shown by the leakage inductor current iLr in Fig. 2

during t4−t5. Therefore, the unnecessary current

increment after the zero current of the power switch is

achieved is minimized. This is an advantage of the

proposed converter since, from full to light-load

conditions, the proposed converter minimizes the

unnecessary current increment after the zero current

of switch Sb is achieved. This will be explained more

detail in the next section. Mode 5 ends when switch

Sx is turned off.

Mode 6 (t5−t0): As switch Sx is turned off, switch Sb

is

turned on with a zero-current condition since the

output diode of switch Sb is in the ON state. The

transferred current to the coupled winding flows

through diode Dx. The voltage across buck inductor

vLb and leakage inductor vLr is given by As shown in

since a large positive voltage is applied to the leakage

inductor, the leakage inductor current iLr increases

rapidly up to the buck-inductor current iLb(t0). This

mode ends when the leakage inductor current iLr

reaches the buck-inductor current iLb.

III. ANALYSIS OF THE PROPOSED

CONVERTER A. ZCS Criteria

Since the ZCS operation of the proposed converter

is always guaranteed only if the duty of Sx is larger

than the interval of t4−t2, the ZCS criterion of the

proposed converter is given by

which is based . Since the time interval t4−t2 has its

largest value at maximum load current Io.max, this

condition should be determined in full-load

conditions.

B. Operation With Load Variation

In the proposed converter, the ZCS operation of

buck switch Sb is achieved by a negative leakage

inductor current iLr. The negative leakage inductor

current increases in very small values after switch

voltage vSb reaches zero current. Therefore, the

unnecessary current increment in negative value is

minimized in light-load conditions and conduction

loss is also minimized. Fig. 4 shows the operation of

the proposed converter in full- and light-load


(NCRACCESS-2015)


conditions, respectively. Since the negative current

flow of iLr makes the ZCS operation of the power

switch possible, the ZCS operation of switch Sb can

be explained with the leakage inductor current iLr

during t3−t5. In this figure, the switch voltage vSb

reaches zero current at time t4. As shown

in Fig. 4(a), since the buck-inductor current iLb has a

large

value at the full-load conditions, the time interval of

t2−t3 is

long. Moreover, during time interval t3−t4, the zero

current of switch Sb is achieved by resonance. After

the voltage of switch Sb reaches zero, the output diode

of Sb remains in the ON state until switch Sb is turned

on with the zero-current condition. Since the duty of

switch Sx is determined by in full-load conditions, the

time interval of t4−t5 can be set as small as possible

in full-load conditions.

Fig. 4(b) shows the operation of the proposed

converter in

light-load conditions. In the practical case, the duty of

switch Sb decreases as the load goes down due to the

parasitics in the circuit. However, the duty of switch

Sx is fixed as the design criteria of and does not

change as the load goes down. Since the buck-

inductor current iLb is small in lightload conditions,

the time interval of t2−t3 is short. Moreover, during

time interval t3−t4, the zero current of switch Sb in

light load condition is achieved in the same resonance

manner as that of full-load conditions. However, since

time interval t2−t3 is short, time interval t4−t5 is long

in light-load conditions. As shown in the vLr

waveform in Fig. 4(b), the leakage inductor voltage

vLr has a very small value as (14) during time interval

t4−t5. Therefore, the leakage current only increases

very slightly in the negative direction even though

time interval t4−t5 is long. Finally, the value of the

negatively increased leakage inductor current in light-

load conditions is very small and a little larger than

that in full-load conditions. Therefore, the

unnecessary current increment is minimized in light-

load conditions after the zero current of switch Sb is

achieved, and the conduction loss is also minimized.

C. DC Characteristics of the Proposed Converter

Applying current-second balance in the buck inductor,

the

voltage conversion ratio of the proposed converter is

given by

Since the duty ratio Da is function of the load current,

the

voltage conversion ratio of the proposed conversion

changes with the load variation. However, its effect is

very small and the voltage conversion ratio

approaches D when Dx and Da are assumed to be very

small.

As shown by the powering current ipo in Fig. 2, the

powering current which is slightly higher than the

buck-inductor current iLb is delivered to the output.

Therefore, there is no increment in the buck-inductor

dc compared with the conventional one and the dc

value of the buck-inductor current ILb.DC is very

slightly lower than Io and can be assumed to be Io,

considering the short period of (Dx + Da)Ts and the

small increment of the powering current during this

period.

IV. DESIGN AND EXPERIMENTAL RESULTS

To validate the characteristics of the proposed

converter, the prototype converter is designed and

tested with the following specifications:

1) input voltage Vin = 100 V dc;

2) output voltage Vo = 50 V;

3) maximum output power Po.(max) = 300 W;

4) switching frequency fs = 125 kHz.

Based on (17), the minimum duty ratio of switch Sx

can be determined according to the leakage inductor

value Lr and coupled-winding turn ratio n, as shown


(NCRACCESS-2015)


in Fig.

The components used in the prototype of the proposed

converter are IRFP250N for switch Sb, MUR2020CT

for

diode Db, IRF640 for switch Sx, MUR2020CT for

diode Dx,

4.2 μH for leakage inductor Lr, 250.66 μH for buck

inductor

Lb, and 0.85 for the coupled-winding turn ratio n. An

EER

4042 ferrite core for buck inductor Lb and a current

density

of 600 A/cm2 for the wire are used. Based on Fig. 5,

the

minimum duty ratio of switch Sx is obtained 0.038,

and 0.05

is used in the experiment for the prototype of the

proposed

converter. Fig. 6 shows the key experimental

waveforms of

the proposed converter in full-load conditions. The

leakage

inductor current iLr is rapidly decreasing and

transferring to the coupled winding during switch Sx’s

on period, and the coupled winding current ix is

rapidly increasing at the same time. The current of

switch Sb has a small negative value at the turn-on

switching moment, and ZCS is observed in the switch

voltage vSb. When the leakage inductor current iLr

increases to the buck-inductor current iLb, the

resonance between the leakage inductor Lr and the

output capacitors of the auxiliary switch and diode

occurs. However, in Fig. 6, the waveforms of vSx and

vDx are not full sinusoidal waveforms even though

they are resonant. This is because vSx and vDx are

clamped to input voltage Vin. Therefore, when one of

vSx or vDx increases to input voltage Vin, the voltage

is clamped to Vin. Fig. 7 shows the ZCS operation of


(NCRACCESS-2015)


switch Sb with load variations. In all load conditions,

the ZCS operation is achieved. In addition, in each

load condition, the switch current does not rapidly

increase in the negative direction and has a very small

negative slope after the zero current of switch current

iSb is achieved. This prevents unnecessary current

increase in the negative direction and minimizes the

conduction loss of the switch and inductor windings.

Fig. 8 shows the zero-current turn-on switching (ZCS)

of switch Sx in the switching moment. Fig. 9 shows

the efficiency comparison of the proposed converter

with the conventional buck converter with load

variations. The proposed converter shows around 95%

efficiency from half- to full-load

conditions,which is around 1.5% higher efficiency

than that of the conventional buck converter. Fig. 10

shows the comparison of the loss distributions of the

proposed and conventional buck converters in full-

load conditions. The proposed converter induces more

losses in the inductor and additional loss in the

auxiliary switch and diode. However, the proposed

converter achieves loss reduction in the buck switch

by soft switching and in the buck diode through the

reduced diode current. Finally, the proposed converter

achieves loss reduction compared with the

conventional buck converter.

Fig. 11 shows the boost counterpart of the proposed

concept. The operation of the boost converter of the

proposed concept is a lot similar to that of the buck

converter. The only difference is that it is based on the

boost converter operation instead of the buck

converter operation.

V. CONCLUSION

This paper has presented the operational

principle, analysis, design example, and experimental

results of the buck converter with a coupled winding,

showing the excellent ZCS operation of the power

switch from heavy- to light-load conditions. Since the

buck-inductor current is transferred to the coupled

winding in a very short period when the auxiliary

switch is in the ON state, the negatively built-up

leakage inductor current of the buck winding

guarantees the ZCS operation of the buck switch in all

load conditions. Furthermore, since the negatively

built up leakage inductor current is minimized after

the zero current

of the buck switch is achieved, the unnecessary

current buildup is minimized and the conduction loss

is also minimized as the load goes down. The basic

operational principle has been presented in the mode

analysis and key characteristics, such as those for the

ZCS operation, and the dc values have also been

presented in the analysis. Based on the design

equations, a prototype converter has been built and

tested. The experimental results of the 300-W

prototype converter prove the key characteristics of

the proposed converter. Around 95% efficiency was

observed from half- to full-load conditions.

REFERENCES [1] Y.-C. Chuang and Y.-L. Ke, “A novel high-efficiency battery

charger with a buck zero-voltage-switching resonant converter,”

IEEE Trans. Energy Convers., vol. 22, no. 4, pp. 848–854, Dec.

2007.

[2] S. Pattnaik, A. K. Panda, and K. Mahapatra, “Efficiency

improvement

of synchronous buck converter by passive auxiliary circuit,” IEEE

Trans.

Ind. Appl., vol. 46, no. 6, pp. 2511–2517, Nov./Dec. 2010.

[3] H.Mao, O. A. Rahman, and I. Batarseh, “Zero-voltage-

switching DC–DC converters with synchronous rectifiers,” IEEE

Trans. Power Electron.,

vol. 23, no. 1, pp. 269–378, Jan. 2008.


(NCRACCESS-2015)


[4] C. S.Moo, Y. J. Chen, H. L. Cheng, and Y. C. Hsieh, “Twin-

buck converter with zero-voltage transition,” IEEE Trans. Ind.

Electron., vol. 58, no. 6, pp. 2366–2371, Jun. 2011.

[5] E. Adib and H. Farzanehfard, “Zero-voltage-transition PWM

converters with synchronous rectifier,” IEEE Trans. Power

Electron., vol. 25, no. 1, pp. 105–110, Jan. 2010.

[6] E. Adib and H. Farzanehfard, “Family of zero current zero

voltage transition PWM converters,” IET Power Electron., vol. 1,

no. 2, pp. 214–223, Jun. 2008.

[7] J.-H. Park and B.-H. Cho, “The Zero Voltage Switching (ZVS)

Critical

Conduction Mode (CRM) buck converter with tapped-inductor,”

IEEE

Trans. Power Electron., vol. 20, no. 4, pp. 762–774, Jul. 2005.

[8] J.-H. Park and B.-H. Cho, “Nonisolation soft-switching buck

converter

with trapped-inductor for wide-input extreme step-down

applications,”

IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 54, no. 8, pp.

1809–1818,

Aug. 2007.

[9] N. Z. Yahaya, K. M. Begam, and M. Awan, “Experimental

analysis of

a new zero-voltage switching synchronous rectifier buck

converter,” IET

Power Electron., vol. 4, no. 7, pp. 793–798, Aug. 2011.

[10] S. Urgun, “Zero-voltage transition-zero-current transition

pulsewidth

modulation DC–DC buck converter with zero-voltage switching-

zerocurrent switching auxiliary circuit,” IET Power Electron., vol.

5, no. 5,

pp. 627–634, May 2012.

[11] J.-M. Wang, S.-T. Wu, and G.-C. Jane, “A novel control

scheme of synchronous buck converter for ZVS in light-load

condition,” IEEE Trans.

Power Electron., vol. 26, no. 11, pp. 3265–3273, Nov. 2011.




Efficient Battery Storage Application for Residential Load

Connected PV System

A.P.Suganya1, Mrs. C.Santhana Lakshmi, M.E, (Ph. D)

2, Dr. C. Nagarajan,B.E, M.Tech, Ph.D

3

1PG Scholar Department of EEE, Sona College of Technology, Salem-5

2Assistant Professor, Department of EEE, Sona College of Technology, Salem-5

3Professor, Department of EEE,Muthyammal Engineering College, Rasipuram

Abstract: This project presents a technique to

control power flowand battery storage for load

connected PV systems in the residential level in

order to optimize the use of PV energy. The

battery is used to supply energy with two

conditions that it should not over charge and it

should not over discharge for the reason of saving

battery life. Here, the battery should be operation

is based on proposed modes and conditions

without any loss.The objective is to increase the

use of battery in order to avoid electricity

purchase from gird during load demand. Because

the use of PV system is increased due to its high

availability. A type of converter is used to

maintain the constant voltage and current. A grid

connected PV system has been arranged to

validate the proposed strategies.A simulation

system was established on the

MATLAB/SIMULINK.

Keywords: Photovoltaic system, buck boost,

converter, SOC algorithm, Battery modes.

1. Introduction

The evolution of fossil fuels reserves and the

ever rising environmental pollution have increased

strongly during last decades the development of

renewable energy sources (RES). The need of having

available continuous energy systems for replacing

gradually conventional ones demands the

improvement of structures of energy supply based

mostly on clean and renewable resources.

Nowadays,photovoltaic (PV) generation is assuming

increased importance as a RES application because of

distinctive advantages such as simplicity of

arrangements, high dependability, no fuel cost, low

maintenance, lack of noise and wear due to the

absence of moving parts. Also the solar energy gives

a clean, pollution free and natural energy source. In

addition to these factors are the limited cost of solar

modules, an increasing efficiency of solar cells,

manufacturing technology improvements and

economies of scale [2].

The increasing range of renewable energy

sources and distributed generators needs new

methods for the operation and management of the

electricity grid so asto keep upor to enhance the

power-supply responsibility and quality.

Additionally, relaxation of the grids ends up in new

management structures, during whichcommercialism

of energy and power is turning intomore and

morevital. The specific application (whether it's off-

grid or grid-connected), battery storage size is

decidedsupported the battery specifications for

optimum charging and discharging rate (units of kW)

and therefore the battery storage capability (units of

kWh). For off-grid applications, batteries ought to

fulfill the subsequent requirements: (i) the

discharging rate should be greater or equal than the

height load capability; (ii) the battery storage

capacity should be large enough to providethe most

importantgetting dark energy use and to be ready to

supply energy throughout the longest cloudy amount.

The IEEE normal provides filler recommendations

for lead-acid batteries in complete PV systems. The

solar array size and therefore the battery size

arechosen via simulations to optimize the operation

of a complete PV system, that considers

responsibility measures in terms of loss of load hours,

the energy loss and therefore the total value. In

distinction, if the PV system is grid-connected,

autonomy could be a secondary goal; instead,

batteries willscale back the fluctuation of PV output

or offer economic benefits like demand charge

reduction and capability firming.

The standalone systems are highly built on

residential households with battery storage. Even at

night and stormy weather conditions, it will able to

provide the energy to the load. A charge controller is

used in the system to prevent overcharging and deep

discharge of the batteries called state of charging.

These systems generally include an inverter, which

converts the DC voltage of PV modules into AC

voltage for direct use with the appliances.At present

grid-connected photovoltaic (PV) systems are




recognized for their contribution to clean power

production.

2. PV system configuration

The simple arrangement of grid-connected PV system configuration is shown in Fig.l. It consists of twopower converters: a DC-DC and a DC-AC converter. Both are bi-directional power converters.

Fig.1 Simple PV system

1. Modelling of PV cells

A photovoltaic (PV) system directly

converts sunlight into electricity. The basic device of

a PV system is the photovoltaic (PV) cell. The

photovoltaic module is the result of associating a

group of PV cells in series and parallel and it

represents the conversion unit in this generation

system. An array is the result of associating a group

of photovoltaic modules in series and parallel. The

obtained energy depends on solar radiation, the

temperature of the cell and the voltage produced in

the photovoltaic module. The voltage and current

available at the terminals of a PV device may

directly feed small.

Fig.2 Equivalent circuit diagram of PV cell

The relationship between the PV cell output

current and terminal voltage according to the single-

diode model is governed by equation (1), (2) and (3).

I = Iph – ID (1)

ID = I0 (2)

I= Iph - I0 (3)

The basic equation (3) of the elementary PV

cell does not represent the I-V characteristic of

practical ones. Practical modules are composed of

several connected PV cells which requires the

inclusion of additional parameters, Rs and Rp, with

these parameters equation (3) becomes equation (4).

I= Iph – I0 (4)

Where Iphis the current generated by the incident

light, ID is the diode current, I0 is the reverse

saturation current, q is the electron charge, k is the

Boltzmann constant, α is the ideality factor. T is the

temperature,.Rs is the series resistance, RP is the

parallel resistance.

2. Modelling of PV array

The number of cells are connected to form a

PV array. The equivalent circuit of PV array are

shown in fig.2

Fig.3 Equivalent circuit diagram of PV array

The current-voltage relationship of PV array is given

by equation (5).

IA = NpIph - NPIRS

+ ∗ (5)

3. DC-AC Converter Power Circuit

The DC-AC converter connected between

DC and AC bus to function either as the inverter to




give power from DC side to the AC side (grid) or as

the PWM rectifier used to recharge the battery from

grid power in particular occasions. The DC-AC

power circuit is constructed from the full bridge

topology as shown in Fig.4

Fig.4 DC-AC power circuit

The converter output is connected to the grid via a

filter inductor and a transformer. A capacitor is

connected with an inductor to form a LC filter for

filtering output voltage in standalone mode.

However, this capacitance should be chosen to be as

small as possible to allow the control system to be

closely real time control. Furthermore, with the

small filter capacitance, the current control loop can

be designed as a simplified L-filter inverter. This

can avoid the instability problem in the system.

4. DC - DC Converter Power Circuit

The power from DC-DC converter bus to

recharge the batteries or to feed the stored energy in

battery to DC bus.Therefore, the power topology of

DC-DC converter is a bidirectional DC chopper is

shown below in fig.5

Fig.5DC-DC converter power circuit

The converter can operate either in buck or

boost mode depending on desired current in battery

(Ibatt) direction. In buck mode, the power from the

DC bus is used to charge the battery, whereas in

boost mode,battery releases the absorbed energy to

DC bus.

III. Battery Modes and Algorithm

The battery should be operated during the

load demand ,but the charging and discharing of

battery should be noted and it based on the SOC

algorithm. The load demand should not be occurred

to which the battery is cut off during the panel

output satisfies the demand if not battery also

combined to satisfies the demand.

A battery is made of one or more cells, it

was either parallel or series connected to obtain a

required current/voltage capability. The voltage of a

battery when at rest (not supplying current) will vary

according to how fully charged the battery.

1. Battery Charging

The rate of charge or current will flow that depend on

the difference between the battery voltage and the

voltage that is inject to battery from solar panel.

While it is beneficial to a battery performance and

life to be fully charged on regular occasions, however

once a battery has been charged to its full capacity, it

is important not to continue charging as this will

damage the battery. A Charge Controller is necessary

to ensure that the battery is not over charged.

Fig.6 Battery charging and discharging

2. Battery discharging

As the battery approaches the fully discharged state,

the voltage starts to fall more quickly again. It is

important for a battery to never be fully discharged,

so your inverter will normally disconnect at 90%

volts. An interesting point to note here is that when

an inverter or other power load is drawing a high

current from the battery, the voltage will drop. This

may mean that the battery needs to be somewhere

over 50% charged to avoid the inverter cutting out

due to low voltage.

3.Battery model

http://www.solar-facts.com/controllers




The most commonly used battery model is

shown in figure it made of an ideal battery with open

circuitvoltage Vo, a constant equivalent internal

resistance Rint and the terminal voltage Vt.

Fig.7 Simplified battery model

The terminal voltage can be obtain from the

open circuit measurement and Rint can be measured

by connecting a load and measuring both terminal

voltage and current, at fully charged conduction this

model does not take into consideration of varying

nature of the internal resistance due to temperature,

state of charge and electrolytic concentration this

kind of model can be used for certain circuit

simulation and not be used for high voltage

applications.

4. Modes and conditions

Mode 1: load demand can only meet by PV. Mode 2:

load demand can be meet by PV and battery.

Condition 1: Battery should be charged upto 90%

during low load. Condition 2: Battery should be

discharged upto 20% during load demand cannot be

meet by PV panel.

5. Battery based on SOC algorithm

To efficiently run the load, the battery

should be charged upto the valid limits. So a need

for measuring the charge of the battery commonly

known as state of charge(SOC). But,a control unit to

be aware of the battery capacity accurately and help

to maintain the state of charge within in the safe

limits. Measuring the battery state of charge (SOC)

is not an easy task because the depends on

temperature, battery capacitance and battery internal

resistance. The charging and discharging of the

battery is constantly taking place, therefore keeping

track of the SOC is an essential butdifficult task.

But, the main objective of the project is to

maintain the state of charge of the battery accurately

and effectively. The approach is done by use of a

linear relationship between Voc and SOCis given by

the equation (6) and (7)

Voc(t) = aS(t) + b (6)

S(t) = [Voc(t) - b] / a (7)

Where s(t) is state of charge ,Voc(t) is the open

circuit voltage ,b is the terminal voltage of battery.

When S(t) = 0% and a is obtained by defining the

value of b and Voc at S(t) 100%. Based on equation

(1),there is a great interest in using open circuit

voltage to determine the SOC of the battery has been

disconnected from the load, which is not possible

during the load demand. To overcome this difficulty,

where the parameter Voc is estimate ever under load

condition. Thereby, we can identify open circuit

voltage and subsequently the SOC of the battery.

The optimization toolbox from MATLAB operates

on measured values current and voltage from the

battery, to find an optimumvalue for the battery, to

find an optimum value for the battery parameters in

order to minimize the error between the calculated

terminal voltage and calculated voltage of the

battery.

1V.SIMULATION RESULTS

The simulation result of the residential load

connected battery storage application. The output

(voltage and current) from the PV panel is shown in

fig (8) & (9).

Fig.8 output voltage from PV panel




Fig 9 output current from the PV system

Fig.10 charging and discharging of the battery

Charging( 90%) and discharging (20%)

Fig.11 converted single phase output for the

residential load

V. CONCLUSION AND DISCUSSION

The guideline to control the power flow

control parameter to achieve the operation for

residential load connected PV system with battery

storage are presented. Here, the excess electricity

generated from the PV system is stored in battery and

electricity must be purchased from the grid if meet

cannot be meet. For latter research the objective is to

reduce the cost by purchasing electricity from grid

and the capacity loss by linear programming and

techno-economical optimization techniques. This will

lead initiated idea for the future research with PV

system.

References

[1]. Power Flow Control and MPPT Parameter

Selection for Residential Grid-Connected PV

Systems with Battery Storage,Chuenwattanapraniti

Chokchai,The 2014 International Power Electronics

Conference.

[2]. Om prakash mahela and sheesh rom ola

,modeling and control of grid connected photo voltaic

system: A review ,IJEEER,Vol. 3, Issue 1, Mar 2013,

123-134.

[3]. Battery Sizing for Grid Connected PV Systems

with Fixed Minimum Charging/Discharging Time.Yu

Ru, Jan Kleissl, and Sonia Martinez are with the

Mechanical and Aerospace Engineering Department,

University of California, San Diego (e-mail:

[email protected],[email protected],[email protected]

du).

[4]. Modeling and Control of Grid Connected

Photovoltaic System- A Review,IJATAE,ISSN 2250-

2459, ISO 9001:2008 Certified Journal, Volume 3,

Issue 3, March 2013.

[5]. Optimal Sizing of Combined PV- Energy Storage

for a Grid-connected Residential Building.Ghassem

Mokhtari*, Ghavam Nourbakhsh*, Arindam

Gosh*,Advances in Energy Engineering (AEE)

Volume 1 Issue 3, July 2013.

[6]. State of charge estimation for batteries, A thesis

for Master of science degree,The university of

Tennessee,Knoxville,December,2002.

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