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A NOVEL APPROACH OF LOAD

FREQUENCY CONTROL IN MULTI

AREA POWER SYSTEM

*V.SHANMUGA SUNDARAM

LECTURER/EEE, SONA COLLEGE OF TECHNOLOGY,SALEM-5, TAMILNADU INDIA

Dr.T.JAYABARATHI

PROFESSOR/DIRECTOR ,SCHOOL OF ELECTRICAL ENGINEERING ,VIT UNIVERSITY

,VELLORE,TAMILNADU INDIA

Abstract:

The main objective of Load Frequency Control(LFC) is to regulate the power output of the

electric generator within an area in response to changes in system frequency and tie-line loading .Thus

the LFC helps in maintaining the scheduled system frequency and tie-line power interchange with the

other areas within the prescribed limits. Most LFCs are primarily composed of an integral controller.

The integrator gain is set to a level that the compromises between fast transient recovery and low

overshoot in the dynamic response of the overall system. This type of controller is slow and does not allowthe controller designer to take in to account possible changes in operating condition and non-linearities in

the generator unit. Moreover, it lacks in roubustness.Therefore the simple neural networks can alleviate

this difficulty.This paper presents the Artificial Neural Network (ANN) is applied to self tune the

parameters of PID controller. Multi area system, have been considered for simulation of the proposed

self tuning ANN based PID controller .The performance of the PID type controller with fixed gain,

Conventional integral controller, and ANN based PID controller have been compared through MATLAB

Simulation results carried out by 1% system disturbances both single area in multi area power system .

Comparison of performance responses of integral controller & PID controller show that the neural-

network controller has quite satisfactory generalization capability, feasibility and reliability, as well as

accuracy in both single and multi area system. The qualitative and quantitative comparison have been

carried out for Integral, PID and ANN controllers. The superiority of the performance of ANN over

integral and PID controller is highlighted.

Keywords: Power system, Artificial Neural network, Back propagation algorithm, PID Controllers.

I.INTRODUCTION

In electric power generation, system disturbances caused by load fluctuations result in changes to

the desired frequency value. 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. Power networks consist of anumber of utilities interconnected together and power is exchanged between the utilities over the tie-lines by

which they are connected. The net power flow on tie-lines is scheduled on apriori contract basis. It is therefore

important to have some degree of control over the net power flow on the tie-lines. Load Frequency Control(LFC) allows individual utilities to interchange power to aid in overall security while allowing the power to be

generated most economically.

The variation in Load frequency is an index for normal operation of the power systems. When the

load perturbation takes place, it will affect the frequency of other areas also. In order to control frequency of the

power systems various controllers are used in different areas, but due to the non-linearity in system componentsand alternators, these conventional feedback controllers could not control the frequency quickly and efficiently.

2..PROBLEM FORMULATION

In order to keep the power system in normal operating state, a number of controllers are used in

practice. As the demand deviates from its normal operating value the system state changes. Different types of

controllers based on classical linear control theory have been developed in the past. Because of the inherent non-linearities in system components and synchronous machines, neural network techniques are considered to build

non-linear ANN controller with high degree of performance.

V.Shanmuga Sundaram et al. / International Journal of Engineering Science and Technology (IJEST)

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Most load frequency controllers are primarily composed of an integral controller. The integrator

gain is set to a level that compromise between fast transient recovery and low overshoot in the dynamicresponse of the overall system. This type of controller is slow and does not allow the controller designer to take

into account possible non-linearities in the generator unit.

2.1 Objectives of LFC:

In order to regulate the power output of the electric generator within a prescribed area in response

to changes in system frequency, tie line loading so as to maintain the scheduled system frequency andinterchange with the other areas within the prescribed limits.

3. MODELING OF SINGLE AREA AND MULTI AREA POWER SYSTEMS

3.1Single Area System Modeling

In Single area system, generation and load demand of one domain is dealt. Any load change

within the area has to be met by generators in that area alone through suitable governor action. Thus we can

maintain the constant frequency operation irrespective of load change.

3.2 Generator Model

A single rotating machine is assumed to have a steady speed of and phase angle 0. Due tovarious electrical or mechanical disturbances, the machine will be subjected to differences in mechanical and

electrical torque, causing it to accelerate or decelerate. We are mainly interested in the deviations of speed, ,

& and deviations in phase angle , from nominal.

This can be expressed in Laplace transform operator notation as

Pmech - Pelec = M s (1.11)

Equation (1.11) can be represented as shown in figure

Figure 1.Relationship between mechanical and electrical power and speed change

3.3 Load Model

The load on a power system comprises of a variety of electrical devices. Some of them are purely

resistive. Some are motor loads with variable power frequency characteristics, and others exhibit quite different

characteristics. Since motor loads are a dominant part of the electrical load, there is a need to model the effect of

a change in frequency on the net load drawn by the system. The relationship between the change in load due to

the change in frequency is given by

PL (freq) = D (or)

D = PL (freq) / (1.12)

1 / M sPmech

Pelec


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The net change in Pelec in figure ( 1.1 ) is

Pelec = PL + D

No frequency frequency

Sensitive load sensitive load

Change change

Incorporating this in the figure2,

Figure 2.Block diagram of rotating mass an load as seen by prime- mover output

3.4 Prime-Mover Model

The prime mover driving a generator unit may be a steam turbine or a hydro turbine. The models

for the prime mover must take account of the steam supply and boiler control system characteristics in the case

of a steam turbine, or the penstock characteristics for a hydro turbine. Here only the simplest prime-mover

model, the non reheat turbine, is considered. The model for a non reheat turbine shown in figure 1.3, relates the

position of the valve that controls emission of steam into the turbine to the power output of the turbine.

Figure3. Prime mover model

1 / M sPmech

PL

D

1 / (M s + D)

PL

Pmech

1/(1+ s T CH )

Pvalve Pmech


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Figure 4. Prime mover generator load model

3.5 Governor Model

Suppose a generating unit is operated with fixed mechanical power output from the turbine, the

result of any load change would be a speed change sufficient to cause the frequency-sensitive load to exactly

compensate for the load change. This condition would allow system frequency to drift far outside acceptablelimits. This is overcome by adding mechanism that senses the machine speed, and adjusts the input valve to

change the mechanical power output to compensate for load changes and to restore frequency to nominal value.

The earliest such mechanism used rotating fly balls to sense speed and to provide mechanical motion in

response to speed changes. Modern governors use electronic means to sense speed changes and often use a

combination of electronic, mechanic and hydraulic means to effect the required valve position changes.

The simplest governor, is synchronous governor, adjusts the input valve to a point that brings

frequency back to nominal value. If we simply connect the output of the speed-sensing mechanism to the valve

through a direct linkage, it would never bring the frequency to nominal.

To force the frequency error to zero, one must provide reset action. Reset action is accomplished

by integrating the frequency (or speed) error, which is the difference between actual speed and desired or

reference speed. Speed-governing mechanism with diagram shown in figure 1.6.1.5 The speed-measurement

devices output, , is compared with a reference, ref, to produce an error signal, .

The error, , is negated and then amplified by a gain KG and integrated to produce a control

signal, Pvalve , which cause main steam supply valve to (Pvalve position) when is negative. If, for

example, the machine is running at reference speed and the electrical load increases, will fall below ref and

will be negative. The action of the gain and integrator will be to open the steam valve, causing the turbine to

increase its mechanical output, thereby increasing the electrical output of the generator and increasing the speed

. When exactly equals ref, the steam valve the new position (further opened) to allow the turbine generator

to meet the increased electrical load.The synchronous (constant speed) governor cannot be used if two or more

generators are electrically connected to the same system since each generator would have to have precisely the

same speed setting or they would fight each other, each trying to pull the systems speed (or frequency) to its

own setting. To run two or more generating units in parallel, the speed governors are provided with a feedback

signal that causes the speed error to go to zero at different values of generator output.

1/(1+ s T CH )

Pvalve Pmech

PL

1 / (M s +D)


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The result of adding the feedback loop with gain R is a governor characteristic as shown in

figure5. The value of R determines the slope of the characteristic. That is, R determines the change on the units

output for a given change in frequency.

The basic control input to a generating unit as far as generation control is concerned is the load reference

set point. By adjusting this set point on each unit a desired unit dispatch can be maintained while holding system

frequency close to the desired nominal value.

R is equal to pu change in frequency, divided by pu change in unit output. That is,

upP

R .

The combined block diagram of single area system with, governor prime mover - rotating

mass/load model is shown in figure7.

1/(1+s Tg )

1/R

0 0.5 1.0

per unit output

Frequency

2 3

Pvalve

Load reference

ref

1. Load reference setting fornominal speed at no load

2. Nominal speed at 0.5 pu output

3. Nominal speed for full output

Figure.5 Block diagram of governor with droop

Figure.6. Governor characteristics


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Figure7.Block diagram of single area system

Suppose that this generator experience a step increase in load ,

s

PsP LL

)(

The transfer function relating the load change PL, to the frequency change is

DMssTsTR

DMssPs

CHG

L1

1

1

1

111

1

)()((1.13)

The steady state value of(s) may be found by

steady state = lim [ s (s)]

s 0

steady state

DR

PL

1 (1.14)

3.6 Multi Area System Modeling

In multi area system load change in one area will affect the generation in all other interconnected

areas. Tie line power flow should also be taken into account other than change in frequency. We will discuss

two area and three area systems in the following session.

3.7 Two Area System

In two-area system, two single area systems are interconnected via the tie line. Interconnection

established increases the overall system reliability. Even if some generating units in one area fail, the generating

units in the other area can compensate to meet the load demand.

GsT1

1

CHsT1

1

DMs

1

1 / R

Load

reference

set point Governor prime mover PL rotating mass

& load

Pvalve


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3.8 Tie Line Model

The power flowing across a transmission line can be modeled using the DC load flow method as

)(1

21 tie

tieflowX

P (1.15)

This tie flow is a steady-state quantity. For purposes of analysis here, we will perturb the above

equation to obtain deviations from nominal flow as a function of deviations in phase angle from nominal.

)(1

)(1

)(1

)]()[(1

21

2121

2211

tie

tieflow

tietie

tie

tieflowtieflow

XP

then

XX

XPP

(1.16)

where 1 and2 are equivalent to 1 and2 .

Then equation (1.16) can be expressed as,

)( 21 s

TP

tieflow

where T is tie-line stiffness coefficient and ,

T = (23.1450) 1 / Xtie (for a 50-Hz system).

Suppose now that we have an interconnected power system broken into two areas each having one

generator. The areas are connected by a single transmission line. The power flow over the transmission line will

appear as a positive load to one area and an equal but negative load to the other, or vice versa, depending on the

direction of flow. The direction of flow will be dictated by the relative phase angle between the areas, which is

determined by the relative speed -deviations in the areas.

A block diagram representing this interconnection can be drawn as in figure 8.


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It should be noted that the tie power flow was defined as going from area 1 to area 2; therefore,

the flow appears as a load to area 1 and a power source (negative load) to area 2. If one assumes that mechanical

powers are constant, the rotating masses and tie line exhibit damped oscillatory characteristics are known as

synchronizing oscillations.

It is quite important to analyze the steady-state frequency deviation, tie-flow deviation and

generator outputs for an interconnected area after a load change occurs. Let there be a load change PL in area 1.

In the steady state after all synchronizing oscillations have damped out, the frequency will be constant and equal

to the same value on both areas.

Then

1 =2 = and

0)()( 21

dt

d

dt

d (1.17)

Finally we have,

21

21

1

11DD

RR

PL

(1.18)

11

1

GsT 11

1

CHsT 11

1

DsM

1 / R1

Load

reference

Pvalve

1

21

1

GsT 21

1

CHsT 22

1

DsM

Loadreference

1 / R2

T / s

2

PL1

PL2

Figure 8. Block diagram of interconnected areas

Ptie

Pvalve


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21

21

2

2

1

11

1

DDRR

DR

P

P

L

tie

(1.19)

The new tie flow is determined by the net change in load and generation in each area. We do not

need to know the tie stiffness to determine this new tie flow, although the tie stiffness will determine how much

difference in phase angle across the tie will result from the new tie flow.

4. BACK PROPAGATION ALGORITHM

Almost any function can be approximated using the multilayer network if we have sufficient number

of neurons in the hidden layer. Infact it has been shown that two layer networks, with sigmoid transfer functions

in the hidden layer and linear transfer functions in the output layer can approximate virtually any function of

interest to any degree of accuracy , provided sufficiently many hidden units are available.

For multilayer networks output of one layer becomes input to the following layer.

Step-1

Propagation of input forward through the network

m

mmmmm

aa

MmforbaWfa

pa

,1,...,2,0)(

,

1111

0

Step -2

Propagation of sensitivities backward through the network

.1,2,...,1,)(*)('

,)(*)('2

11

MformsWnFs

atnFs

mTmmmm

MMM

Step -3

Updation of weights and biases using approximate steepest descent rule

.)()1(

,)()()1(1

mmm

Tmmmm

skbkb

askWkW

Basic steps of using MATLAB toolbox, nntool .

In this thesis training is carried out using the two methods. However the updation of weights and

biases from the initial set values, sensitivities, trained data, coordination between the target and trained datacant be explicitly in the second method (using nntool). NNTOOL method provides the facility to train through

one of the methods Say conjugate gradient method, Levenberg-Marquardt method for back propagation. It is

superior to approximate steepest descent method. Hence at first training is carried out using the nntool method.

In the neural network we have employed TANSIG as transfer function in the hidden layer and PURELIN in theoutput layer. Then the obtained weights and biases are chosen as the initial weights and biases. Now the training


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is carried out using the first method (using approximate steepest descent method). All results such as the

updating of weights and biases from the initial set values, sensitivities, trained data, coordination between thetarget and trained data can be obtained.

A. Design of ANN Controller:

The range over which error signal is in transient state, is observed. Responding values of theproportional, integral and derivative constants are set. This set is kept as target. Range of error signal is taken as

the input.

This input target pair is fed and new neural network is formed using nntool in the MATLABSimulink software. Weights and Biases obtained are fed to back propagation algorithm using approx. steepest

descent method. Thus the neural network is well trained. Updated weights and biases are given to a fresh neural

network. Now the neural network is ready for operation.In ANN controller is trained with the set of data to determine the PID controller parameters:

Proportional constant, KP

Integral constant, KI

Derivative constant, KD.

Normal PID controller has fixed constants for proportional, integral and derivative term .Butduring transient state controller effect can be made to perform better by variation of the constants. This is

accomplished by ANN controller.

5.SIMULATION STUDY:

5.1 Single Area LFC:

Figure.9. Comparison of PI, PID And ANN Controllers for single area system

5.2 Multi Area LFC: Area 1

Figure10. Comparison of PI, PID And ANN Controllers for multi area 1system


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5.3Multi Area LFC: Area 2

Figure11. Comparison of PI, PID And ANN Controllers for multi area 2 system

Inference: From the above responses of Figure1,2&3 shows that comparison of PI , PID And ANN Controller

which gives the ANN Controller less undershoot and settling time to other controllers.

PERFORMANCE INDICES

CONTROLLER IAE ISE ITAE

INTEGRAL 0.1495 0.0065 0.3496

PID 0.1000 0.0018 0.3409

ANN 0.0951 0.0016 0.3159

Table 1. Single Area LFC

MULT1 AREA LFC

PERFORMANCE INDICESCHANGE IN

FREQUENCY IN Hz

IAE ISE ITAE

INTEGRAL

f1 0.1793 0.0072 0.5572

f2 0.2932 0.0142 1.2304

PID

f1 0.0443 8.2026e-004 0.0602

f2 0.0513 0.0014 0.0649

ANN f1 0.0487 8.0973e-004 0.0799

f2 0.0577 0.0014 0.0885

Table 2. Multi Area LFC


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MULTI AREA LFC: TIE-LINE FLOW

PERFORMANCE INDICES

CHANGE IN

FREQUENCY IN Hz

IAE ISE ITAE

INTEGRAL

tie 0.3306 0.0132 1.9921

PID

tie 0.0789 8.8451e-004 0.3469

ANN

tie 0.0784 8.9196e-004 0.3339

Table 3. Multi Area LFC Tie-Line floe

With the reference to the results obtained as shown in Table1. we can conclude that theperformance indices IAE/ISE/ITAE are minimum both Area 1 and Area 2 .When ANN controller compared to

that of Integral and PID controllers.

6.CONCLUSION

From the simulation results obtained for load disturbances for ANN controller, PID controller,Conventional integral controller we can conclude that ANN controller is faster than the other, Peak undershoot

is reduced, Settling time is reduced. The superiority of ANN controller is established in the cases of two area

systems. From the Qualitative and Quantitative comparison of the results we can conclude that the ANNcontroller yields better results. ANN controller gives minimum IAE/ISE/ITAE compared to the conventional

integral and PID controllers. The simulation studies were also done for change in the operating conditions like

change in governor time and turbine time constants. The Qualitative comparisons of all the controllers show thatthe ANN results in robust performance. Hence ANN controller has large potential to be used as a control

strategy for the Load Frequency Control.

V.REFERENCES

[1] Aanstad, O. J. and Lokay, H. E. (1970) Fast valve control can improve turbine generator response to transient disturbances.Westinghouse Engineer, July, pp. 114-119.

[2] Dr.Chaturvedi D.K Prof.Satsangi P.S & Prof.kalra P.K, Application of Generalized Neural Network to Load Frequency ControlProblem.

[3] Djukanovic, M., Sobajic, D. J. and Pao, Y. H. (1992) Neural net based determination of generator-shedding requirements in electricpower systems.IEEE Proceedings--C139, 5 427-436.

[4] Elgerd OI. Electric energy systems theory: An introduction. McGraw-Hill;[5] Kundur.P (1994) Power System Stability and Control New York McGraw-Hill[6] Hadi Saadat Power System Analysis Tata Mcgraw-Hill Publishing Company Limited New Delhi.[7] Hertz, J., Krogh, A. and Palmer, R. G. (1991) Introduction to the Theory of Neural Computation. Addison-Wesley, Reading, MA.[8] Ha QP. A fuzzy sliding mode controller for power system load-frequency control. In: IEEE Second Conf -Knowledge-Based

Intelligent Systems, 1998

[9] Jaleeli N, VanSlyck LS, Ewart DN, Fink LH,Hoffmann AG. Understanding automatic generation control. IEEE Trans Power Systems1992;7(3):110612.

[10] Kanniah J, Tripathy SC, Malik OP, Hope GS. Microprocessor-based adaptive load-frequency control. IEE Proc C1984; 131(4):1218.[11] Pan CT, Liaw CM. An adaptive controller for power system load-frequency control. IEEE Trans Power Systems 1989;4(1):1228.[12] Reddoch P, Julich TT, Tacker E. Model and performance functional for load frequency control in interconnected power systems. In:

IEEE Conf Decision and Control, 1971.

[13] Talaq J, Al-Basri F. Adaptive fuzzy gain scheduling for load frequency control. IEEE Trans Power Systems 1999;14(1):14550.[14] Vajk I. Adaptive load-frequency control of the Hungarian power system. Automatica 1985; 21(2):12937.[15] Yamashita K, Miyagi H. Multivariable self-tuning regulator for load frequency control system with interaction of voltage on load

demand. IEEE Proc D 1991; 138(2).


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APPENDIX:

Parameters Area 1 Area 2

Turbine time constant 0.5 s 0.6s

Generator Time constant 0.2s 0.3s

Generator Angular

Momentum

10 MJrad/s 8 MJrad/s

Governor Speed Regulation 0.05 pu 0.065 pu

Load change for Frequency

change of 1% D = p / f

0.6%

0.6

0.9%

0.9

Rated output 250 MW 250 MW

Sudden Load Variation 250 MW

250/250=1p.u

250 MW

250/250=1p

BIOGRAPHY:

V.Shanmuga Sundaram. received the M.E. degree in Power Systems Engineering from Periyar University,

Salem Tamilnadu, India, in 2005. He is a research Scholar of VIT University, Vellore ,Tamilnadu India.Currently, he is an Lecturer in Sona College of Technollogy Salem, His interests are in power system controldesign and Deregulated power systems.

Dr. T.Jayabharathi received the Ph.D., degree in Power Systems Engineering from Anna University , Chennai

in 2003. She has published more than 30 journals of both national and international. Currently she is an Director

and Senior professor of School of Electrical Engineering in VIT University, Vellore. His research interests arePower system optimization, Soft computing ,Economic dispatch control problems and Deregulated power

systems.


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