The Effect of Fuzzy Logic Controller on Power

System Stability; a Comparison between Fuzzy Logic

Gain Scheduling PID and Conventional PID

Controller M. Ahmadzadeh, and S. Mohammadzadeh

Abstract---This paper presents the application of a fuzzy Logic

controlled to improve stability of power system. The system in this study is a two-area electrical interconnected power system. Also, in this paper, the effect of a conventional controller PID, and a fuzzy logic gain scheduling PID (FGPID) controller on power system stability are compared. The performance of these controllers in First, settling times and overshoots of the frequency deviation are

compared. All the models are simulated by Matlab Simulink software. The simulation results show that the FL controller developed in this study performs better than the PID controller with respect to the settling time and overshoot, and absolute error integral of the frequency deviation.

Keywords---Load–frequency control, Power system stability;

Fuzzy logic controller

I. INTRODUCTION

OW–frequency oscillations are a common problem in

large power systems. Increasing power demand leads to

more complexity and less reliability of interconnected

power systems. Insufficient transmission capability of the

interconnection leads to bottle necks in the system and reduce the system stability. In an electric power generation,

disturbance caused by load fluctuation

Will result in changes of the desired operating frequency [1-

2]. The requirements for control of frequency in an

interconnected power system are implemented by the

Automatic Load Frequency Control (ALFC). The ALFC

provides automatic variation of generation set points on the

speed governors to maintain system frequency within the

specified limit. Intelligent controller is a Fuzzy Logic

Controller for a Frequency Stabilization.

Control algorithms based on fuzzy logic have been

implemented in many processes. The application of Such control techniques has been motivated by the following

reasons: 1) improved robustness over the Conventional linear

control algorithms; 2) simplified control design for difficult

system models; 3) simplified implementation [1], [4],

[7].Several studies have been done in the past about the load–

frequency control in interconnected power systems.

M.Ahmadzadeh, Department of Electrical Engineering, Mahshahr branch,

Islamic Azad University, Mahshahr, Iran (corresponding author to provide

phone: +989132535199, mostafa.ahmadzadeh@yahoo.com)

S.Mohammadzadeh, Department of Electrical Engineering, Mahshahr

branch, Islamic Azad University, mahshahr, Iran.

In the literature, a number of control strategies have been

suggested based on the conventional linear control theory. To

some investigators, variable structure system control [4, 5]

maintains stability of system frequency. However, this

method needs some information for system states, which are very difficult to obtain completely. Also, some described a

minimum variance strategy for load frequency control of

interconnected power systems [6]. According to [7],

conventional PID control schemes will not reach a high

performance. Since the dynamics of a power system even for

a reduced mathematical model is usually non-linear, time

variant and governed by strong cross-couplings of the input

variables the controllers have to be designed with special care

[8]. Thus, a gain scheduling controller can be used for

nonlinear systems [3]. In this method, control parameters can

be changed very quickly because parameter estimation is not

required. It is easier to realize as compared with automatic tuning or adaptation of controller parameters. However, the

transient response can be unstable because of abruptness in

system parameters. Besides, it is impossible to obtain accurate

linear time invariant models at variable operating points [3].

Some fuzzy gain scheduling of PI controllers have been

proposed to solve such problems in power systems [3] and [8]

that developed different fuzzy rules for the proportional and

integral gains separately. Fuzzy logic control presents a Good

tool to deal with complicated, non-linear and indefinite and

time-variant systems [7]. In this paper, the rules for the gains

are chosen to be identical in order to improve the system performance. The comparison of the proposed FGPID and the

conventional PID suggests that the overshoots and settling

time with the proposed FGPID controller are better than the

PID controller.

II. SYSTEM MODEL

In an interconnected network, a disturbance in one line or

changing in loads, leads to effects on the neighboring systems

to change in frequency causing severe problem in the entire power system network. LFC is very important in power

system operation and control for supplying sufficient and

adequate electric power with good quality. Power system

have not been designed for wide area power trading with daily

varying load patterns where power flows do not follow the

initial planning criteria of the existing network configuration.

L

International Conference on Computer, Systems and Electronics Engineering (ICSCEE'2014) April 15-16, 2014 Johannesburg (South Africa)

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Interconnected power systems naturally consist of complex

and multi-variable structures with many different control

blocks. They are usually non-linear, time-variant and/or non-

minimum phase systems [8]. Power systems are divided In to

control areas connected by tie lines. In each control area, the

generators are supposed to constitute a coherent group. It means that the movements of their rotors are closely related

[4]. Experiments on the power systems show that tie-line

power flow and frequency of the area are affected by the load

changes at operating point. Therefore, it can be considered

that each area needs its system frequency and tie-line power

flow to be controlled [3]. Additionally, it is desired

That transient frequency oscillation without a large increase

in the magnitude and speed control must be reduced. Also, the

number of LFC signals sent to power systems without

compromising other objectives must be reduced [8].

Since the small load changes are affected by the active

power, and the frequency, while reactive power is only affected by the magnitude of the bus voltage, a separate

control loop can be used for frequency control. Generally, the

load–frequency control is accomplished by two different

control Actions in interconnected two-area power systems: (a)

the primary speed control and (b) supplementary or secondary

speed control actions. The primary speed control performs the

initial vulgar readjustment of the frequency. By its actions,

the various generators in the control area track a load

variation and share it in proportion to their capacities. The

speed of the response is only limited by the natural time lags

of the turbine and the system itself. Depending upon the turbine type the primary loop typically responds within 2–20

s. The supplementary speed control takes over the fine

adjustment of the frequency by resetting the frequency error

to zero through an integral and differential actions. The

relationship between the speed and load can be adjusted by

changing a load reference Set point input. In practice, the

adjustment of the load reference set point is accomplished by

operating the speed changer motor. The output of each unit at a given system frequency can be varied only by changing its

load reference, which in effect moves the speed-droop

characteristic up and down. This control is considerably

slower and goes into action only when the primary speed

control has done its job. Response time may be of the order of

one minute. The speed-governing system is used to adjust the

frequency. Governors adjust the turbine valve/gate to bring

the frequency back to the nominal or scheduled value.

Governor work satisfactorily when a generator is supplying an

isolated load or when only one generator in a multi generator

system is required to respond to the load changes. For power

and load sharing among generators connected to the system, speed regulation or droop characteristics must be provided.

The speed-droop or regulation characteristic may be obtained

by adding a steady-state feedback loop around the integrator.

Ш. Controller Design

An uncontrolled two-area interconnected power system is

shown in Fig. 1, where f is the system frequency (Hz), Ri regulation constant (Hz/unit), Tg speed governor time

constant (s), Tt turbine time constant (s) and Tp is power

system time constant (s).

Fig.1. A two-area interconnected power system (DP1, 1, 2: load demand increments)[1]

The objective of the proposed controller design is to

improve the power system performance under the normal and

different load disturbance. To maintain the system frequency

in an interconnected power system, the controller is designed using a fuzzy logic gain scheduling PID to improve the power

system stability. The overall system can be modeled as a

multi-variable system in the form of:

[ ]

T

T

Where A, B and E are system state matrix, distribution

matrix and disturbance matrix of appropriate dimensions

respectively. Similarly x, u and d are the state, control and

disturbance vector and ∆ denotes deviation from the nominal values and u1 and u2 are the control outputs in Fig. 1.

The system output, which depends on area control error

(ACE) shown in Fig. 2, is given as:

International Conference on Computer, Systems and Electronics Engineering (ICSCEE'2014) April 15-16, 2014 Johannesburg (South Africa)

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The prime objective is to minimize the Area Control Error

(ACE) which stabilizes the system frequency for a sudden

load disturbance. The objective function of the load frequency

controller [8, 7, and 1] is given by,

Where i – Number of areas, ΔF- Change in frequency, ΔPtie

– Change in tie-line power and β – biasing factor.[1,2]

Fig.2.Two-area power system with controller [7]

IV. FUZZY LOGIC IN POWER SYSTEMS.

Fuzzy set theory and fuzzy logic establish the rules of a non-linear mapping [6]. The use of fuzzy sets provides a basis

for a systematic way for the application of uncertain and

indefinite models [5]. Fuzzy control is based on a logical

system called fuzzy logic. It is much closer in spirit to human

thinking and natural language than classical logical systems

[7]. Nowadays, fuzzy logic is used in almost all sectors of

industry and science. One of them is the load frequency

control [1]. The main goal of the load–frequency control in

the interconnected power systems is to protect the balance

between production and consumption. Because of complexity

and multi-variable conditions of the power system, conventional control methods may not give satisfactory

solutions. On the other hand, Robustness and reliability make

fuzzy controllers useful in solving wide range of control

problems [1]. The fuzzy controller for the single input–output

type of systems is shown in Fig. 3 [4].

Fig.3. the simple fuzzy controller. [1]

V. FUZZY GAIN SCHEDULED PID CONTROLLER

By taking ACE as the system output, the control vectors for

the conventional PID controller can be given in the following

form:

∫

(

)

( )

∫ ( ) (

)

Power systems are shown that the conventional controllers

have large overshoots and long settling times [4,1]. Also, optimizing time for control parameters, especially PID

controllers, is very long and the parameters are not calculating

exactly. In addition, it has been known that conventional

controllers generally do not work well for non-linear, higher

order and time-delayed linear, and particularly complex and

vague systems that have no precise mathematical models [1].

According to many researchers, there are some reasons for the

Present popularity of fuzzy logic control. First, fuzzy logic

can easily be applied to most industrial applications in

industry.

Fig.4. Membership functions for FGPI Controller of (a) ACE, (b)

∆ACE, (c) KP, Ki. KD

Second, it can deal with intrinsic uncertainties by changing controller parameters. Finally, it is appropriate for rapid

applications. Therefore, fuzzy logic has been applied to the

industrial systems as a controller. Human experts prepare

linguistic descriptions as fuzzy rules, which are obtained

based on step response experiments of the process, error

International Conference on Computer, Systems and Electronics Engineering (ICSCEE'2014) April 15-16, 2014 Johannesburg (South Africa)

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signal, and its time derivative [8]. Determining the controller

parameters with these rules, the fuzzy gain scheduling

proportional, integral and differential controller (FGPID) is

formed. Fuzzy logic shows experience and preference through

membership functions, which have different shapes

depending on the experience of system experts [2]. Same

inference mechanism is realized by seven rules for the two

FGPID controllers. The appropriate rules used in the study are

given in Table 1.

TABLE I

FUZZY LOGIC RULES FOR FGPI CONTROLLERS

∆ACE(k)

ACE (k) LN MN SN Z SP MP LP

LN LP LP LP MP MP SP Z

MN LP MP MP MP SP Z SN

SN LP MP SP SP Z SN MN

Z MP MP SP Z SN MN MN

SP MP SP Z SN SN MN LN

MP SP Z SN MN MN MN LN

LP Z SN MN MN LN LN LN

Membership functions shapes of the error and derivative error

and the gains are chosen to be identical with triangular

function for both fuzzy logic controllers. However, their horizontal axis ranges are taken different values because of

optimizing these controllers. The membership function sets of

FGPI for ACE, ∆ACE, Kp and Ki are shown in Fig.

4,.Defuzzification has also been performed by the center of

gravity method in all studies.

VI. THE MODEL SIMULATION

Simulations were performed using the conventional PID

and the proposed FGPID controllers applied to a two-area

interconnected electrical power system. The same system

parameters [3], given in Table 2 were used in all controllers

for a comparison. Two performance criteria were selected in

the simulation. The frequency deviation graphs were first

plotted with Matlab Simulink software. (Fig.5 and 6). Here,

settling times and overshoots of the frequency deviation of the

controllers were compared against each other.( given in Table

3)

TABLE II

TWO-AREA POWER SYSTEM PARAMETERS

Tg=0.08 B1=0.425

R1=2.4 B2=0.425

R2=2.4 T12=0.086

TP=20 KP=120

Tt=0.3 a12 =-1

TABLE III

SYSTEM PERFORMANCES FOR ALL CONTROLLERS ON SETTLING TIMES AND

OVERSHOOTS FOR FREQUENCY DEVIATION OF AREA 1 (FOR 5% BAND OF

THE STEP CHANGE)

Maximum

overshoots(Hz) SETTLING TIME (S)

CONTROLLERS

-0.0205 4.1 FGPID

-0.0256 5.7 conventional PID

The comparison results are provided in Table 3. In the

analysis of the simulation results, the frequency Comparison

of the proposed controller with conventional PID controller

shows, system response with the proposed controller has a

quite shorter settling time and lower magnitude in overshoots.

Fig5. Deviation of frequency of area 1 with conventional PID

controller (DPd, i = 0.01 p.u.).

Fig 6. Deviation of frequency of area 1 with GPID controller

(DPd, i = 0.01 p.u.).

Simulations were performed for different instantaneous loads changes and Success was achieved in all cases. As

shown in Table 3, the settling time of the proposed FGPID

controller is substantially shorter conventional PID controller

and the proposed controller has the minimum integral

absolute error too. Therefore, the proposed controller is better

than other controller.

VII. CONCLUSIONS

In this paper, a FGPID (fuzzy gain scheduling of PID) controller, In order to automatically load frequency controller,

For an interconnected power system was Examined.in fuzzy

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simulation The number of rules for the inference mechanisms

was taken seven, so that the controller performances were

improved by increasing the rule numbers to 49. The results

show that the proposed algorithm is effective in controlling

and improves system performance. The proposed controller is

much easier and requires no information about the system parameters. Based on experimental results, It's performance is

better than the other controller in settling time and Integral

absolute error and in over shoot is close to the optimum. As a

result, the proposed fuzzy gain scheduling PID controller is

recommended to generate good quality and reliable electric

energy.

ACKNOWLEDGEMENTS

This work was supported by Islamic Azad University, mahshahr Branch, Iran

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Mostafa ahmadzadeh was born in Shiraz, Iran, in 1983.

He received the B.S. degree from yazd university ,yazd ,

iran in 2006 and M.Sc. degree from chamran university,

ahvaz, iran in power engineering in 2009.

He is currently with department of Electrical Engineering,

Mahshahr branch, Islamic azad university, mahshahr, Iran

as lecturer. his research interests include stability of power

systems, design of FACTS devices and power quality.

Saeed Mohammadzadeh was born in 1984 in Lahijan,

Iran. He received B.Sc. degree in power electrical

engineering from Guilan University, Iran, in 2006, and

M,,Sc. degree in power electrical engineering from

Shahid Chamran University, Iran, in 2009.

He is currently with department of Electrical

Engineering, Mahshahr branch, Islamic azad university,

mahshahr, Iran as lecturer. His current research interest includes power

quality Detection and Control.

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