AC 2012-3302: SMART CONTROL OF POWER ELECTRONIC CONVERT-ERS IN PHOTOVOLTAIC SYSTEMS

Mr. Ahmed Mohamed, Florida International University

Ahmed Mohamed (El-Tallawy) was born in Minia, Egypt, in 1984. He received his B.Sc. degree fromthe faculty of engineering, Minia University, Minia, Egypt, in 2006. From 2006 to 2009, he was a Re-search/Teaching Assistant in the faculty of engineering, Minia University. He received a M.Sc. degreefrom the faculty of engineering, Minia University, Minia, Egypt in 2009. He is currently a Research As-sistant in the Electrical and Computer Engineering Department, College of Engineering and Computing,Florida International University, Miami, Fla., USA. His current research interests are DC distribution,renewable energy systems, and electrical power systems. Address: Energy Systems Research Labora-tory, Electrical and Computer Engineering Department, College of Electrical and Computer Engineering,Florida International University, 10555 W Flagler Street, Room EC-3925, Miami, Florida 33174, USA.Telephone: +1 305-348-6194; Cell +1 786-975-4524.

Dr. Osama A. Mohammed, Florida International University

Osama A. Mohammed received his M.S. and Ph.D. degrees in electrical engineering from Virginia Poly-technic Institute and State University. He has many years of teaching, curriculum development, research,and industrial consulting experience. He authored and co-authored more than 300 technical papers in thearchival literature, as well as in National and International Conference records in addition to additionalnumerous technical and project reports and monographs. Mohammed specializes in electrical energy sys-tems, especially in areas related to alternate and renewable energy systems. He is also interested in designoptimization of electromagnetic devices, artificial intelligence applications to energy systems, and elec-tromagnetic field computations in nonlinear systems for these energy applications. He has current interestin shipboard power systems and integrated motor drives. He is also interested in the application communi-cation and sensor networks for the distributed control of power grids. Mohammed has been successful inobtaining a number of research contracts and grants from industries and federal government agencies. Hehas current active and funded research programs in several areas funded by the Office of Naval Researchand the U.S. Department of Energy. Mohammed is also interested in developing learning environmentsand educational techniques for Internet based delivery systems and virtual laboratories. Mohammed is aFellow of IEEE and is a recipient of the 2010 IEEE PES Cyril Veinott Electromechanical Energy Con-version Award. Mohammed is also a Fellow of the Applied Computational Electromagnetic Society. Heis Editor of IEEE Transactions on Energy Conversion, IEEE Transactions on Magnetics-Conferences,as well as an Editor of COMPEL. He also received many awards for excellence in research, teaching,and service to the profession and has chaired sessions and programs in numerous international confer-ences in addition to delivering numerous invited lectures at scientific organizations in around the world.Mohammed serves as the International Steering Committee Chair for the IEEE International Electric Ma-chines and Drives Conference (IEMDC) and the IEEE Biannual Conference on Electromagnetic FieldComputation (CEFC). Mohammed was the General Chair of the 2009 IEEE IEMDC conference held inMiami Florida, May 3-6, 2009, and was the Editorial Board Chairman for the IEEE CEFC2010 held inChicago, Ill., USA, May 9-12, 2010. He was also the General Chairman of the 1996 IEEE InternationalConference on Intelligent Systems Applications to Power Systems (ISAP’96). Mohammed has chairedthe Electric Machinery Committee for IEEE PES and was the Vice Chair and Technical Committee Pro-gram Chair for the IEEE PES Electric Machinery Committee for a number of years. He was a member ofthe IEEE/Power Engineering Society Governing Board (1992-1996) and was the Chairman of the IEEEPower Engineering Society Constitution and Bylaws committee. He also serves as Chairman, Officer oras an active member on several IEEE PES committees, sub-committees, and technical working groups.

c©American Society for Engineering Education, 2012

Page 25.1163.1

Smart Control of Power Electronic Converters in Photovoltaic Systems

Abstract

This paper presents an overview of the techniques used to control the power electronic

converters used to integrate renewable energy sources to the electric grid. Moreover, a smart

fuzzy-PID controller for DC-DC boost converters, which are the most commonly used

converters as voltage regulators in Photovoltaic (PV) systems, is presented. Details about the

educational side of these concepts; in-class, simulation and experimental demonstrations are also

included. The proposed fuzzy-PID controller maximizes the stable operating range by tuning the

PID parameters ultimately at various loading conditions. Then, a fuzzy logic approach is used to

add a factor of intelligence to the controller such that it can detect a change in the operating point

and move among different values of proportional gain (Kp), integral gain (Ki) and derivative gain

(Kd) based on the system conditions. This controller allows optimal control of boost converters at

any loading condition with no need to retune parameters or possibility of failure. Moreover, the

paper presents the methodology to teach a novel technique to move between the PI and PID

configurations of the controller such that the minimum overshoot and ripple are obtained, which

increases the controller applicability for utilization of PV systems in supplying sensitive loads.

This paper gives an excellent educational presentation of several aspects related to control

theory, renewable energy engineering and power electronics.

Introduction

Photovoltaic systems have become globally accepted as a practical and feasible tool for power

generation. Researchers’ efforts for facilitating PV systems utilization and their integration to

currently available systems have been always inspired by the national goal of having renewable

and clean energy sources. These efforts successfully solved many of the problems that are

attached to PV systems [1].

One of the major problems of PV systems is that the output voltage of PV panels is highly

dependent on solar irradiance and ambient temperature. Hence, loads cannot be directly

connected to the output of PV panels.

A DC-DC converter is required to operate as an interface between PV panels and loads [2]. The

DC-DC boost converter fixes the output voltage of the PV system. It receives a variable input

voltage, which is the output of PV panels, and yields a constant output voltage across its output

capacitors where the loads can be connected. Normally, a DC-DC boost converter operates at a

certain duty cycle. In this case, the output voltage is at a certain level corresponding to that duty

cycle. If the input voltage is changed while the duty cycle is kept constant, the output voltage

varies. However, in the controlled boost converter utilized in this paper, the duty cycle is

controlled based on the input voltage and loading conditions such that the output voltage is

constant. Duty cycle is varied using a pulse width modulation (PWM) technique.

PID controllers are commonly used as controllers for boost converters in PV systems. However,

these controllers have to be tuned according to a certain operating range and loading conditions.

This limits the operating range of the controller. In this paper, the operating range of the

controller is maximized by tuning the PID controller parameters; Kp, Kd and Ki at different

operating points. A fuzzy controller [3, 4] is then used to set the optimal values of the controller

parameters based on the measured output current. The controller will be utilized in this paper to

output a proper modulation index for pulse width modulation.

Page 25.1163.2

Due to this exponentially increasing importance of PV systems, power electronic converters and

their corresponding controllers, the topic of PV energy and its integration to electric grids is an

essential topic to be taught to engineering students. In this educational paper, teaching the topic

will start with some general background about PV systems and power electronic converters. This

portion is theoretical and will be explained by the instructor in class. Then, the

MATLAB/SIMULINK power system simulation tool will be used to simulate and test the

developed converter and controller before it is implemented in hardware. This portion needs a

computer lab or in the form of assignments to students depending on their knowledge about

power simulation tools. Finally, students will implement the controller in hardware and test in

during real-time operation.

Photovoltaic Systems

Solar arrays consist of different solar cells connected in series and/or parallel in order to

achieve desired voltage and current levels. Solar cells consist of semi-conductor materials that

have the ability to convert solar irradiation into DC current. This is called the PV effect. The

characteristic equation of solar arrays is given by (1) [2],

{ [

( )] }

(1)

Where:

ILG Light generated current

IOS Reverse saturation current

q Electronic charge

A Dimensionless factor

K Boltzmann’s constant

Rs Series resistance of the cell

Rsh Shunt resistance of the cell

The equivalent circuit of the PV panel is given in Fig. 1. Inspecting the characteristic

equation of PV panels given in (1), we can see that the relation between output voltage and

current of PV panels is not linear. Therefore, the output voltage of PV panels is dependent on the

amount of power drawn out of them. Moreover, the output voltage of PV panels is dependent on

solar irradiation and ambient temperature. Hence, the output voltage of PV panels is variable

depending on these conditions. On the other hand, a constant voltage level is needed for

connecting loads to PV arrays. This imposes an imperative necessity of having a power

conditioning unit as an interface between PV panels and the loads connected to them.

Fig. 1. PV cells equivalent circuit model.

𝐼𝐿𝐺

𝐼𝐷 𝐼𝐿𝐺

𝐼

𝑅𝑆𝐻

𝑅𝑆

Page 25.1163.3

Fig. 2 shows the electric characteristics of the PV panels. It can be seen that fluctuations in

the output current (ΔI), result in fluctuations in the output voltage (ΔV), which lead to

fluctuations in the output power (ΔP). The figure indicates that any power fluctuations around

the maximum power point (MPP) derate the average power coming from the PV array.

In a typical PV system as shown in Fig. 3, the PV array is connected to a boost converter. As

a result of the switching process, the input current to the boost converter will oscillate around its

DC value, these oscillations result in current fluctuations at the PV side, which derate the array’s

power. As a consequence, the loadability of the array decreases which is not desired.

In order to reduce the fluctuations at the PV array side, and increase the system loadability, a

capacitor can be used to smoothen the output current and voltage profile of the array, a capacitor

of capacitance Cpv=1200 µF has been used in this paper.

Fig. 2. Power and current characteristics of the PV panels versus voltage

Fig. 3. A typical stand-alone PV system.

Power Electronic Converters

Power electronic converters are essential components of PV systems since PV arrays yield a

variable DC output voltage. If the output voltage from PV systems is higher than the required

voltage level, a DC-DC buck converter is used to step down the voltage. The step-down

converter is as shown in Fig. 4. The power electronic switch is connected in series with the input

MPPT Algorithm

& PID control

𝐼𝑝𝑣 𝑣𝑝𝑣 𝑃𝑊𝑀

𝐶𝑝𝑣

𝐹𝑢𝑠𝑒 𝐿

𝐶 𝐶𝐴𝐶

𝐿𝐴𝐶

𝑅𝐿𝑜𝑎𝑑

PLL &

PID Control

NOT NOT

𝐴 𝐵

𝐴 𝐵

𝑣𝑜𝑢𝑡

PV Array Boost Converter

DC AC Inverter

Filter

Page 25.1163.4

voltage source. Hence, when it switches ON and OFF the output voltage is stepped-down

corresponding to the duty cycle (D), which is the ratio between the time within which the IGBT

is ON to the switching time. An output filter consisting of a capacitor and an inductor is then

used to filter the DC output. The output voltage is given by (2)

V DV (2)

where Vout is the output voltage of the converter, Vin is the input voltage and D is the duty

cycle.

Fig. 4. Buck converter circuit

On the other hand, if the output voltage from PV arrays is to be stepped up, a boost converter is

most commonly used. Hence, we focus on this example in this paper. Boost converter is a DC-

DC converter that steps up its input voltage based on the formula given in (3)

V

V (3)

The circuit diagram of the boost converter is shown in Fig. 5. It consists of an inductor, an IGBT

switch, a fast switching diode and a capacitor. The configurations of the boost converter circuit

during switching ON and OFF intervals are shown in Figs. 6 and 7, respectively. When the IGBT

is switched ON ( t t ), the inductor is directly connected to the input voltage source. In

this case, the inductor current rises charging it and the inductor is storing energy while the diode

is reverse biased disconnecting the load (RL) and output capacitor (C) from the source voltage.

During this interval, the pre-charged capacitor assures constant voltage across the load terminals.

When the IGBT is switched OFF (t t ) where Ts is the switching period, the diode is

forward biased and both the source and the charged inductor are connected to the load. The

inductor releases the energy stored in it. This energy is transferred to the load in the form of

voltage that adds to the source voltage. Hence, the converter boosts the input voltage.

Fig. 5. Boost converter circuit

Fig. 6. ON state of the IGBT

Page 25.1163.5

Fig. 7. OFF state of the IGBT

The boost converter proposed in this paper is designed to operate in the continuous conduction

modes (CCM) which means that the inductor current is always higher than zero. The inductance

value is designed to be higher the minimum inductance required for operation in CCM given by

(4) [5]

( )

(4)

where Lmin is the minimum inductance, D is the duty cycle, RL is load resistance, and fs is the

switching frequency of the IGBT.

The capacitance is designed such that the output voltage ripple is within the desired boundary.

The minimum capacitance required for certain output voltage ripple is given by (5)

C

(5)

The duty cycle governs how much boosting of the input voltage will be achieved during boost

converter operation. In other words, by controlling the duty cycle we can output constant output

voltage even in the case of input voltage or loading variation.

Theoretical Background on Smart Fuzzy/PID (In-class presentation)

In order to control the output voltage of the boost converter, proportional/integral/derivative

(PID) controllers are commonly used to give a duty cycle command signal to the switch. The

derivative part can be neglected from the PID controller, so the controller in this case is called a

PI controller as shown in Fig. 5. The PID gains are designed and tuned using the transfer

function of the converter. The duty cycle to output voltage boost converter’s transfer function is

highly nonlinear. Therefore, it has to be linearized around a limited operating range to design the

controller using linear control theory.

Normally, a DC-DC boost converter operates at a certain duty cycle. In this case, the output

voltage corresponds to that duty cycle. If the input voltage is changed while the duty cycle is

kept constant, the output voltage will vary. However, in the controlled boost converter utilized in

this paper, the duty cycle is controlled based on the input voltage and loading conditions such

that the output voltage is constant. Duty cycle is varied using a pulse width modulation (PWM)

technique. Hence, the system under study in this section is as shown in Fig.3 except that the

inverter stage is eliminated since the power is delivered in DC form. The main objectives of the

proposed controller are,

1. Maximizing the Operating Range

1.1 Tuning the PID parameters

The operating range of the boost converter is mainly defined by the input voltage and the output

current. In order to maximize the operating range of the control system, the parameters of the

Page 25.1163.6

PID controller are tuned at different operating points. The tuning process aims at minimizing rise

time, settling time, ripple and steady state error of the output voltage of the boost converter

corresponding to step changes in input voltage and load. Parameters were tuned at values of

around 60º phase margin and 10 dB gain margin. The tuning process yields values of Kp, Ki, Kd

that are tuned at different output current ranges and input voltage ranges. For instance, results of

the tuning process at different loading conditions at an input voltage of 110 V are given in Table

1.

Figs. 8 and 9 show the response of the PID controller corresponding to a step change in the

loading condition using two different techniques; firstly in Fig. 8 when the PID controller has the

same parameters values before and after the change in loading condition. Secondly, Fig. 9 shows

the response for the controller when the parameters vary corresponding to Table 1 such that a

matching set of (Kp, Ki and Kd) constants are used after the step change is applied. We can see

that the response in Fig. 9 is better in terms of overshoot (75% less) and less ripple.

Table 1: Kp, Ki and Kd optimal values at input voltage of 110 V and different output current ranges

Current range (A) Kp Ki Kd

0.00-0.80 0.00200 0.2000 0.000000

0.80-1.36 0.00940 0.6216 1.1765e-4

1.36-1.76 0.00830 0.6216 1.1913e-4

1.76-2.24 0.00320 0.6214 7.0000e-4

2.24-3.00 0.00270 0.5300 2.7420e-5

3.00-5.0 0.00128 0.4042 1.4527e-4

Fig. 8. Load step change voltage response for conventional PI controller, 110 V input voltage.

Fig. 9. Load step change voltage response for proposed controller, 110 V input voltage.

1.2 Fuzzy controller involvement for smart decision making

Fuzzy control is a powerful control method that can be applied on different systems. It is based

on the experience of the user about the system behavior rather than modeling the system under

0 0.25 0.5 0.75 1

182

186

190

194

198

202

Time (sec)

Voltage (

Volts)

0 0.25 0.5 0.75 1

182

186

190

194

198

202

Time (sec)

Vo

lta

ge

(V

olts)

Page 25.1163.7

control mathematically like in linear control theory. This makes fuzzy control a powerful control

technique especially with non-linear systems in which it is difficult to derive an accurate

approximated mathematical model of the system and expect its behavior. Fuzzy control is a rule-

based control technique that is approached by linguistic fuzzy rules, which describe the output

desired out of the system under different operating conditions. Fuzzy rules are in the form of if-

then rules that the proficient should design such that they cover all the conditions the system is

expected to go through.

Designing a fuzzy logic controller is achieved through three basic steps; fuzzification, inference

Mechanism and defuzzification as shown in Fig. 10. The Mamdani type fuzzy system has been

used in this paper.

In fuzzification, different membership functions are used to map the input variables, which are

the output current and the PV voltage into fuzzy sets. Each of the output current and the input

voltage that are the inputs to the control system is mapped into six fuzzy subsets as shown in Fig.

11. Operation of the membership functions on the input variable yields the extent to which that

variable is a member of a particular rule. The process of converting control variables into

linguistics rules is called fuzzification.

The fuzzy controller has three outputs which are Kp, Ki and Kd. Each is fuzzified into a

membership function and mapped into a certain linguistic values. The Kp values are represented

by five membership functions whereas each of the Ki and Kd is represented by four membership

functions as shown in Fig. 12.

However, in inference Engine and Rule base step, the output of fuzzy controller is managed

through putting certain linguistic rules. These control rules are constructed based on given

conditions (inputs) such that the fuzzy controller decides the proper control action. The control

action here means that the controller outputs a suitable Kp, Ki and Kd gains such that the PID

controller parameters are those which give the optimal performance at every operating range.

The rules are designed such that the controller gives the values of the PID parameters suitable for

the current loading condition.

Fig. 10. A block diagram of the fuzzy controller utilized in this paper.

(a) (b)

Fig. 11. Membership functions for: (a) output current, (b) PV voltage

0 50 100 150 2000

0.2

0.4

0.6

0.8

1

Input voltage (Volts)

Mem

bers

hip

0 1 2 3 4 50

0.2

0.4

0.6

0.8

1

Output Current (Amps)

Mem

bers

hip

Fuzzification Inference Engine Defuzzification

Rule Base

Crisp Input 1

Crisp Input 2

VB SB SM VS M SB S B B S BB MB

Page 25.1163.8

(a)

(b)

(c)

Fig. 12. Membership functions for: (a) Kp gain, (b) Ki gain and (c) Kd gain

Finally, in defuzzification, as the output of the fuzzy controller is in the form of fuzzy set, it has

to be transformed from linguistic form into a number that can be used to control the system.

Many defuuzification methods like weighted average (wtaver) or weighted summation (wtsum)

1 2 3 4 5 6 7 8

x 10-3

0

0.2

0.4

0.6

0.8

1

Kp Gain

Mem

bers

hip

VS VBMS B

0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.60

0.2

0.4

0.6

0.8

1

Ki Gain

Mem

bers

hip

VS S M B

0 1 2

x 10-4

0

0.2

0.4

0.6

0.8

1

Kd Gain

Mem

bers

hip

VS S BM

Page 25.1163.9

methods have been proposed. In this paper, we utilized the wtaver method. Figs 13-15 show the

output of the fuzzy controller as a function of the output current and input voltage.

Fig. 13. Surface plot of the Kp gain

Fig. 14. Surface plot of the Ki gain

Fig. 15. Surface plot of the Kd gain

2. Enhancing Transient and Steady State Response

PI controller is the most commonly used controller in industry. It is simply a PID controller in

which the derivative gain value is set to zero. Generally, the proportional integral (PI) controller

is able to control a DC-DC boost converter. On the other hand, the derivative part of the PID

controller has the characteristic of anticipating the future behavior of the error as it deals with the

derivative of the error. Hence, it is very helpful in mitigating sudden and step changes that the

system may be subjected to. However, it causes ripple in the output voltage waveform. Fig. 16

12

34

5

50

100

150

2000

0.005

0.01

Io

Vin

Kp

12

34

5

50

100

150

2000

0.2

0.4

0.6

0.8

Io

Vin

Ki

12

34

5

50

100

150

2000

2

4

6

x 10-4

Io

Vin

Kd

Page 25.1163.10

shows the response of the PID controller for the same case study given in Fig. 8. As can be seen

in the figure, adding the derivative component (PID controller) decreases the voltage dip caused

by the step change of the load. However, the ripple increases during steady state operation in this

case more than it is in the case of the PI controller after.

Fig. 16. Load step change voltage response for conventional PID controller

In this paper, we benefit the help of the derivative part only when it is useful to have it as a part

of the controller and eliminate its effect gradually until we obtain a PI controller with minimum

ripple. The technique is based on detecting any step change in the input variables using a

derivative block. This transient detection pulse triggers the Kd gain. The derivative gain is used

to mitigate the transient effect then it is gradually excluded from the controller to reduce the

ripple in steady state operation. A block diagram of the proposed controller is shown in Fig. 17.

Fig. 17. Block diagram of the proposed controller

Computer Lab (Simulating the developed converter)

Students will simulate the DC-DC boost converter to examine the difference between the

performances of the proposed controller and of the conventional converter and run some

experiments to verify the validity of the proposed controller and gain experience on dealing with

PV systems, power electronic converters and PID controllers. The MATLAB/SIMULINK

simulation tool will be used. In order to do that, students need a computer lab with MATLAB

0 0.25 0.5 0.75 1

182

186

190

194

198

202

Time (sec)

Vo

lta

ge

(V

olts)

𝐶𝐷𝐶

𝐿𝐷𝐶

𝐴

𝑉 𝑅𝐿𝑜𝑎𝑑 𝑣𝑝𝑣

Kd Controller Load variation

detector

X

X

X

𝑠

𝑠

𝑉

𝑣𝑟𝑒𝑓

𝑒

𝐾𝑝

𝐾𝑖

𝐾𝑑

𝑣𝑝𝑣 𝑖𝑜

𝑖𝑜

Fuzzy

Page 25.1163.11

2010 or a newer version installed including SimPowerSystems library and the Fuzzy Logic

Toolbox. The procedure to build the simulation model is as follows,

1- Open MATLAB.exe.

2- Open SIMULINK tool.

3- FileNewmodel (a new empty model will now be open).

4- SimulationConfiguration parameters: adjust sampling time Ts to 1e-5 and the solver to

a fixed step solver ode1 (Euler).

5- Open SIMULINK library.

6- From the library find then drag and drop all the hardware components and measurements

(IGBT, diode, inductor, capacitor, resistive load, voltage and current measurements) from

the SimPowerSystems library.

7- From the SIMULINK main different libraries, find the various gains and math operations

needed to develop a PI controller. For instance, the gains from Math Operations library

and the integral from the Continuous library.

8- Build the circuit then connect a scope to the output of the voltage measurement.

9- Run the model and apply step changes to the voltage reference and monitor the

converter’s performance.

10- Replace the conventional PI controller with the developed controller by adding the fuzzy

agent from the Fuzzy Logic Toolbox.

Finally, the SIMULINK model is as shown in Fig 18.

Fig. 18. Simulink model of a controlled DC-DC boost converter.

Fig. 19 shows the voltage, current and PID gains responses to a load step change from 100 W to

500 W. Load current is an input for the fuzzy controller and based on its value, the PID

parameters are estimated. As can be seen in the figure, the Kp and Ki gains changed

instantaneously with the load step change with a small delay of 0.3 msec. It can also be observed

that a pulse is generated with a period that is a function of the capacitor time constant to detect

any loading to the boost converter. At steady state operation, the gain Kd in this case causes the

capacitor to have a slow charge/discharge operation. However, this is undesired in steady state

Controlled DC-DC Boost Converter Circuit

Sawtooth

shifter

Continuous

powergui

[V]

V_act1

[V]

V_act

Sowtooth

SaturationReference voltage

[G]

Pulses1

[G]

Pulses

PV EmulatorLoad

L

20

Ki

1

s

Integrator

gm

CE

IGBT

-K-

Gain1

D

> 0

Compare

To Zero

Cv

+-

error Duty Sy cle

Page 25.1163.12

operation. On the other hand, one of the advantages of having a fast controller is its ability to

enhance the transient response of the converter corresponding to changes in the output voltage.

Therefore, the Kd gain is zero at steady state operation. However, at any loading or input voltage

change, it is applied to a tuned value. This value is chosen by the fuzzy logic controller. It can be

observed from Fig. 19 that the Kd gain has a fixed value of 0.7e-3 during the load detection pulse

ON period. Once the load detection pulse is turned off, an exponential damping factor is applied

to the Kd value to enhance the ripple of the device at the steady state operation. However, the Kp

and Ki values are fixed.

Fig. 19. Proposed adaptive controller load step change, 100 W-500W, response and controller parameters variations.

A PI controller is used in comparison with the proposed adaptive controller to highlight its

advantages. Fig. 20 shows the voltage and current responses corresponding to a load step change

of 100 W to 500 W. The same load step changes were applied previously to the adaptive

controller illustrated in Fig. 19. The Y-axis ranges used in Fig. 20 for the voltage and current are

the same as in Fig. 19 to facilitate the comparison. When the load suddenly changes from 100 W

to 500 W, the voltage dip in the case of traditional PI controller is around 19 V. Whereas, this

voltage dip is around 1 V in case of the proposed controller.

Fig. 20. Traditional PI controller Load step change response (100W-500W), simulation results.

Page 25.1163.13

Experimental Lab (Implementing the developed converter in real-time)

Experimental results will also been taken to insure the validity of the proposed control

strategy. This requires a power system lab. The following components will be needed,

PV Emulator

dSPACE 1104

Current Transducer

Voltage Transducer

Inductor

Capacitor

IGBT/Diode

Heat Sink

Fig. 21. Components needed to build the controlled boost converter circuit.

The test setup used will be as shown in Fig. The tests are as follows; firstly, the load is

changed from 220 W to 1 KW. As seen in Fig. 23, a transient of 0.05 sec has occurred and a

small voltage dip can be observed. Another test was conducted to test the controller. A load step

change from 1 KW to 220 W was applied to the boost converter. Due to that step change, a

transient of 0.05 sec occurred. In addition, a voltage overshoot of around 1 V is observed during

that transient. Fig. 24 shows the voltage and current response for a load step change from 1 KW

to 220 W.

To illustrate the effect of having the derivative gain in the controller, Fig. 25 shows the voltage

and current transient responses corresponding to a load step change of 1 KW to 220 W. Here, the

Page 25.1163.14

fuzzy controller here is choosing only the PI parameters. Whereas, the derivative gain value is

set to zero at all loading conditions. It can be observed that a voltage dip of approximately 10

Volts occurred when the load was switched and it took the controller 0.35 sec to stabilize. These

experimental results indicate that when adding the derivative gain component in the PID

controller during the transient interval, a better transient response is achieved.

Figs. 26 and 27 show the voltage and current transient response of the traditional PI controller

for the same step changes from 220 W to 1 kW and from 1 kW to 220 W, respectively. It can be

seen that there is voltage undershoot and overshoot of around 20 V in both case studies,

respectively.

Fig. 22. Experimental setup for the controlled DC-DC boost converter.

Fig. 23. Proposed adaptive PID controller response for load step change from 220 W to 1 KW, experimental results

dSPACE 1104

dSPACE Control desk

Programmable Power

Supply (PV Emulator)

Boost Converter

DC Load AVO Meter

Page 25.1163.15

Fig. 24. Proposed adaptive PID controller response for load step change from 1 KW to 220 W, experimental results.

Fig. 25. Proposed controller (with Kd gain set to zero) response for load step change from 220 W to 1 KW,

experimental results.

Fig. 26. Traditional PI controller response for load step change from 220W to 1 KW, experimental results.

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Fig. 27. Traditional PI controller response for load step change from 1 kW to 220 W, experimental results.

Conclusion

In this paper, a smart PID controller has been presented. Teaching this developed controller

involves concepts related to various subjects including renewable energy sources, power

electronics and control theory. Moreover, the simulation and experimental environments used to

develop and verify the developed controller can very effective tools to increase students’

knowledge about these subjects.

References

[1] T. Khatib, A. Mohamed and N. Amin, “A new controller scheme for photovoltaics power generation systems,” European Journal of

Scientific Research, vol. 33, no. 3, pp. 515-524, 2009. [2] J. Santos, F. Antunes, A. Chehab and C. Cruz, “A maximum power point tracker for PV systems using a high performance boost

converter,” Solar Energy, vol. 80, pp. 772-778, 2006.

[3] C. Elmas, O. Deperlioglu, H. Sayan, “Adaptive fuzzy logic controller for DC-DC converters,” Expert Systems with Applications, vol. 36, pp. 1540-1548, 2009.

[4] P. Mattavelli, L. Rossetto, G. Spiazzi and P. Tenti, “General-purpose fuzzy controller for DC-DC converters,” IEEE Transaction on Power

Electronics, vol. 12, no. 1, pp. 79-86, 1997. [5] M. H. Rashid, Power Electronics Handbook, California: ACADEMIC PRESS, 2001.

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