Proceedings of the International conference on Electrical Energy systems (ICEES-2013)

Department of Electrical and Electronics Engineering, SSN College of Engineering, Tamilnadu-India

GENETIC ALGORITHM BASED PI CONTROLLER FOR LOAD

FREQUENCY CONTROL IN POWER SYSTEMS

Ch. V. Narasimha Raja #Department of Electrical Engineering

ANIL NEERUKONDA INSTITUTE OF TECHNOLOGY AND SCIENCES (ANITS),

Sangivasa, Visakhapatnam. 1ch.v.n.raja@gmail.com

ABSTRACT Frequency and generation control is the major problem in interconnected power networks. If

network frequency is changed over 10%, units can leave from the synchronization with network.

These changes are to be impossible the network interconnection. To improve the constant

frequency, second level of automatic generation control scheme that is used to improve the

frequency fixing and called as secondary or supplementary control is the aim of this paper. So,

genetic algorithm is proposed in this study. It is shown from the computer simulations, proposed

method optimize the PI parameters and decrease the frequency oscillation.

Keywords: Load frequency control, genetic algorithm, generation control.

I. INTRODUCTION Frequency is a major stability criterion for large-scale stability in multi area power

systems. To provide the stability, active power balance and constant frequency are

required. Frequency depends on active power balance. If any change occurs in active

power demand/generation in power systems, frequency cannot be hold in its rated value.

So oscillations increase in both power and frequency. Thus, system subjects to a serious

instability problem. To improve the stability of the power networks, it is necessary to

design a load frequency control (LFC) systems that control the power generation and

active power at tie lines. Because of the relationship between active power and frequency,

three level automatic generation controls have been proposed by power system researchers

[1, 2]. In interconnected power networks with two or more areas, the generation within

each area has to be controlled so as to maintain scheduled power interchange.

Load frequency control scheme have to be two main control loops. These are primary

control and secondary control [1]. This action is realized by turbine-governor system in the

plant. In this control level, only active power is balanced. However, maintaining the

frequency at scheduled value (e.g. 50 Hz) cannot be provided. Therefore, steady state

frequency error can occur forever. So this level does not enough for interconnected system.

In interconnected power systems, frequency must be equal at all areas. The second level of

generation control called as secondary or supplementary control is happened in large

power systems which include two or more areas. Active power is controlled at the tie line

between neighbor areas and there are central and local load control and distribution center.

Especially, large power plants join to frequency control due to the fact that high power

plants such as Atatürk Power Plant in Turkiye, lead the other plants. The final level of

generation control is economic dispatch among the all plants. The major aim of this control

is to maintain each unit’s generation at the most economic value [1, 2].

Up to now, there have been developed several controllers for load frequency control by

using novel and intelligent control techniques. These controllers have given good results in

load frequency control. In ref [3] layered neural networks for nonlinear control of power

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system is applied. A Feed-forward neural network is proposed to control of the steam

turbine in this study. Another neural network (NN) controller is experienced in ref [4] by

using long training times and a great number of neurons. It is demonstrated the availability

of an adaptive optimal load frequency controller using NNs and fuzzy set theory in a two-

area power system in ref [5]. This control system is based on the pattern recognition

principle and in implementation on the parallel-distributed computational architecture of

NNs. Furthermore other controllers which are based on the optimization the parameters of

PI and PID have been proposed. In ref [6], PID parameters were changed using fuzzy

based gain scaling technique. Also fuzzy gain scheduling technique was applied to load

frequency control in [7-11]. Also dynamic wavelet neural network and fuzzy neural

network were experienced to design adaptive load frequency controllers in [12, 13]. In this

study, PI parameters are improved by using the genetic algorithms and developed PI

controller is applied to a two-area power system. Proposed controller successfully damps

the frequency oscillations and restores the system frequency.

II. LOAD FREQUENCY CONTROL IN TWO AREA NETWORKS II.1. Major Principals of LFC

In multi area power networks the active power generation within each area should be

controlled so as to maintain scheduled power interchange [1]. Nowadays computer-aided

controllers realize this action. Simulated system seen in Figure 1 consists of an

interconnection of two power areas. Primary and secondary control loops are also

illustrated in Figure 2. Both of them are connected each other by a tie line. Power between

areas flows throughout the tie line. Control and balance of power flows at tie line are

required for supplementary frequency control. Also damping of oscillations at tie line is

another requirement for successful control of frequency and active power generation. For

the simulations, linearized mathematical model given in Figure 3 is used.

Fig.1 Interconnection between two areas

Fig.2 Principal Scheme for Primary and Secondary Control Levels

Area -1 Area -2 P12

X12

Turbi

ne

w

To

networ

k

steam

valve

slope

Primary

Control

Control Unit

(PI)

f, Ptie

from

other

areas

Secondary Control

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Fig.3. Linearized Block Diagram of Two Area Power Systems

II.2. Tie Line Power Interchange and Linearized Model

Consider the interconnected two area power system shown in Figure 1. Power areas are

connected each other via a transmission line called as tie line. Tie line reactance is

represented by X12. Equivalent circuit diagram without consider the dynamical oscillations

between machines in both areas is given in Fig.4.

Figure 4 Simplified Equivalent Diagram of Two Area Power System

Power flow from area 1 to area 2 throughout tie line is calculated by following equation.

11E 22E

Xeq1 Xeq2 X12

P12

XT=Xeq1+X12+Xeq2

Governor

&

Turbine 1 1P

1P

sT1

K

+ +

1/R1

B1

-

Governor

&

Turbine 2 2P

2P

sT1

K

+ +

Proportional

Integral

Proportional

Integral

1/R2

B2

-

+

-

+

-

- -

f1

f2

Ptie

PL1

PL2

+ T/s

-

1205

)(Sin.X

E.EP 21

T

2112

(1)

After linearization, power change in tie line is found by Eq.2.

)).((Cos.X

E.ETP 212010

T

21

12 (2)

Above power flow deviation depends on frequency (f) and rotor angle deviation () can

be calculated by Eq.3 and 4 in time domain and –s domain.

dt.fdt.fT2P 2112 (3)

)s(f)s(fs

T2)s(P 2112

(4)

Mathematical representation of two area power systems given in Figure 3 is modeled by

Eq.5 in this paper.

BUXAX .&

(5)

State variables are chosen as following form.

T

2I2G2T2M21I1G1T1M1 Ptie] X X X P f X X X P f[X (6)

III. TUNING OF PI PARAMETERS BY USING GENETIC

ALGORITMA

III.1. Genetic Algorithms

Genetic algorithms are stochastic computational methods which are inspired from

evolution. They are used for optimization problems, scheduling applications and design

optimizations. Genetic algorithms encode a potential solution to a specific problem on a

chromosome-like data structure and apply recombination operators to these structures so as

to preserve critical information. It is available to reach good solutions by using a little

information. After the evaluation process, generated solution space is transformed to

another space which consists of the point or points that give good results. This

transformation is achieved by the genetic operators such as Selection, Crossover and

Mutation. Solutions consist of chromosomes. Each chromosome represents a possible

solution for the optimization of the problem and a value for some variables of the problem

called as “gene”. Genetic operators are natural processes and critical criterias affected to

these operators have to be defined during the creation of the algorithm [14-16]. The natural

selection of strings (chromosomes) is mimicked by selection operator. Hence, it is created

a new generation where the most suitable members are reproduced most frequently.

Crossover is the combination between chromosomes of the selected parent. After crossover

operation, new members are created. Crossover and genetic codes of new members are

given:

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Parents Children

100011001 100010101

011010100 011011000

Mutation is the situation that unexpected genes are occurred in new generation members

Probability of mutation must be less than crossover. Its probability is one of the criterions

of evolution like probability of crossover. This criterion influences evolution results. There

is an example of mutation appeared in chromosome of child that consists binary coded

genes :

Child Child

100011101 100011001

Evaluation process can be summarized by the attached flow diagram in Appendix-A

III.2. Tuning of PI Parameters using GA

Genetic algorithms are used to minimize error criteria of PI (Proportional-Integral) in each

iteration. The integral square error (ISE) is used to define the PI controller’s error criteria.

This criterion is formulated in Eq. (6). At first, Physical system is represented as a set of

differential equations. Than these equations is used to evaluate the system responses. The

responses are calculated by using ISE equation.

T

o

2 dt.)]t(y)t(r[ISE (6)

Where r(t) is the reference input and y(t) is the measured value. In each iteration, the

fitness values of each member are evaluated by the results of Eq.(6). These fitness values

are used to select best parents from population.

III.3 Computer Simulations

In computer simulations, frequency deviations are investigated due to changing in active

power generation of +10% in Area-1. Results are given in following figures comparatively.

It can be shown from the figures; proposed secondary controller damps the frequency

oscillations in both areas compared to integral controller in Figures 5 and 6. To achieve

this, power flow in tie line between areas is increased (Figure 7) and power balance in both

areas is provided. In first peak of the frequency cannot be damped due to governor control

time delay but than secondary controller activates and successfully decreases the peaks and

oscillations compared to integral controller.

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0 10 20 30 40 50 60 70 80

-0.4

-0.2

0

0.2

0.4

0.6

0.8

time (sec)

frequency d

evia

tion (

pu)

Area 1

Integral

Proposed

Figure 5 Frequency deviation in Area-1 (10% decrease in power demand)

0 10 20 30 40 50 60 70 80

-0.4

-0.2

0

0.2

0.4

0.6

time (sec)

frequency d

evia

tion (

pu)

Area 2

Integral

Proposed

Figure 6 Frequency deviation in Area-2 (10% decrease in power demand)

0 10 20 30 40 50 60 70 80-0.04

-0.035

-0.03

-0.025

-0.02

-0.015

-0.01

-0.005

0

0.005

time (sec)

Ptie (

pu)

Integral

Proposed

Figure 7 Tie lie power deviation between areas (10% decrease in power demand)

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IV. CONCLUSION

In this paper, a PI controller which its parameters can be changed using genetic algorithm

is presented.

Genetic algorithm is an applicable method in power system in particular control and

stability. Load frequency control investigated in this study has recently come into question

in operation of interconnected power networks. Frequency is a sensitive parameter which

affects the system operation so it is controlled certainly. Therefore power utilities consider

the frequency and active power balance throughout their networks to sustain the

interconnection. In interconnection between national/continental networks, providing the

constant frequency between areas is a serious operational problem. Hence fast and no delay

decision-making mechanism have to be installed in network control units.

Genetic algorithm proposed in this study, is a modifiable and improvable method and it

can be obtained better results for load frequency control applications. In next studies,

authors will be kept on to get more sensitive and successful results.

V. REFERENCES

[1] Kundur P, “Power System Stability and Control”, McGraw-Hill, NewYork 1994.

[2] Wood AJ, and Wollenberg BF, “Power Generation Operation and Control”, 2nd

Edition, John Wiley and Sons, New York, 1996.

[3] Beaufays F, Abdel-Magid Y, and Widrow B, Application of neural networks to load

frequency control in power systems, Neural Networks, vol. 7,pp. 1–194,1994.

[4] Chaturvedi DK, Satsangi PS, Kalra PK, Load frequency control: a generalized neural

network approach. Int J Electr Power Syst., vol. 21, pp. 6–415, 1999.

[5] Djukanovic M, Novicevic M, Sobajic DJ, Pao YP, Conceptual development of optimal

load frequency control using artificial neural networks and fuzzy set theory. Int J Eng Intell

Syst Electr Eng Commun, vol. 3, pp. 2–108, 1995.

[6] Oysal Y, Köklükaya E, and Yılmaz, AS, Fuzzy PID controller design for load

frequency control using gain scaling technique, Powertech Conference Proceedings,

Budapest, Hungary 1999.

[7] Kocaarslan I., and Çam E, Fuzzy logic controller in interconnected electrical power

systems for load-frequency control , Int.J. of Electrical Power and Energy Systems, vol.

27, pp. 542–549, 2005.

[8] Çam E, and Kocaarslan I., Load frequency control in two area power system using

fuzzy logic controller, Energy Conversion and Management vol 46 pp. 233–243, 2005.

[9] Çam E, and Kocaarslan I., A fuzzy gain scheduling PI controller application for an

interconnected electrical power system, Electric power System Research, Electric Power

Systems Research vol. 73 pp 267–274, 2005.

[10] Chang CS, Fu W. Area load-frequency control using fuzzy gain scheduling of PI

controllers. Electric Power System Research, vol. 42, pp. 145–152, 1997.

[11] Talaq J, Al-Basri F. Adaptive fuzzy gain scheduling for load-frequency control. IEEE

Trans Power Systems vol. 14, no 1, pp.145-150, 1999.

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[12] Oysal Y, Yılmaz AS, and Köklükaya E, A dynamic wavelet network based adaptive

load frequency control in power systems, Int.J. of Electrical Power and Energy Systems

vol. 27 pp. 21–29, 2005.

[13] Yusuf Oysal, Ahmet Serdar Yilmaz, Etem Koklukaya,Adaptive Load Frequency

Control with Dynamic Fuzzy Networks in Power Systems, Lecture Notes in Computer

Science vol.3512, pp. 1108-1115, Springer-Verlag, 2005

[14] Whitley D, Genetic algorithms and evolutionary computing, Van Nostrand's Scientific

Encyclopedia-2002.

[15] Whitley D,, A Genetic algorithm tutorial , statistics and computing Vol.4pp.65-85,

1994.

[16] Miranda V, Srinicasan D, Proenca LM, Evolutionary computation in power systems,

Int. J. of Electric Power and Energy Systems, Vol.20, No.2, pp.89-98, 1998.

Appendix-A

Create initial

population

Crossover and

mutation

Select parents

Reproduction and

create new population

Has

criteria

achieved

Yes

No

Create PI controller

using last population

and solve system

Optimum

population

Measure fitness

values

Fig. A.1 GA Flow Diagram