Seminar Final Report

AUTOMATIC GENERATION CONTROL USING FUZZY CONTROL

CONTENTS

1. INTRODUCTION 2

2. FUZZY LOGIC 4

3. FUZZY LOGIC CONTROLLER 5

4. SYSTEM UNDER STUDY WITH FUZZY

LOGIC CONTROLLER 9

5. APPLICATION OF FUZZY LOGIC TO AUTOMATIC

GENERATION CONTROL 11

6. ALGORITHM OF FUZZY LOGIC TO AGC PROBLEM 11

7. FUZZY RULE BASE AND INTERFERENCE 13

8. AUTOMATIC GENERATION CONTROLLER 14

9. ADVANTAGES 18

10.CONCLUTION 19

11.REFRENCES 20

DEPT of E&E, VVCE, MYSORE Page 1


1. Introduction

Automatic generation control (AGC) plays a very important role in power

system as its main role is tomaintain the system frequency and tie line flow

at their scheduled values during normal period.Automatic generation control

with primary speed control action, a change in system load will result in a

steady state frequency deviation, depending upon governor drop

characteristics and frequency sensitivity of the load.

All generating units on speed governing will contribute to overall change in

gyration, irrespective of the location of the load change. Restoration of the

system frequency to nominal value requires supplementary control action

which adjusts the load reference set point. Therefore the primary objectives

of the automatic generation control are to regulate frequency to the nominal

value and to maintain the interchange power between control areas at the

scheduled values by adjusting the output of selected generators. This

function is commonly referred to as load frequency control. A secondary

objective is to distribute the required change in generation among the units

to minimize the operating costs.Generation in large interconnected power

system comprises of thermal, hydro, nuclear, and gas powergeneration.

Nuclear units owing to their high efficiency are usually kept at base load

close to theirmaximum output with no participation in system automatic

generation control (AGC). Gas powergeneration is ideal for meeting varying

load demand. However, such plants do not play very significantrole in AGC of

a large power system, since these plants form a very small percentage of

total systemgeneration.Gas plants are used to meet peak demands only.

Thus the natural choice for AGC falls on either thermal or hydro units.An

interconnected power system can be considered as being divided into

control areas which are connected by tie lines. In each control area, all

generator sets are assumed to form a coherent group. The power system is

subjected to local variations of random magnitudes and durations, Hence, it



is required to control the deviations of frequency and tie-line power of each

control area. In actual power system operations, the load is changing

continuously and randomly. As the ability of the generation to track the

changing load is limited due to physical/technical considerations, there

results an imbalance between the actual and the scheduled generation

quantities. This imbalance leads to a frequency error i.e. the difference

between the actual and the synchronous frequency. The magnitude of the

frequency error is an indication of how well the power system is capable to

balance the actual and the scheduled Published in International Journal of

Advanced Engineering & Applications, Jan. 2010 58 generation. The presence

of an actual-scheduled generation imbalance gives rise initially to

systemfrequency excursions in accordance to the sign of the imbalance and

act to reduce the magnitude of actual scheduled generation imbalance.

A control signal made up of tie line flow deviation added to frequency

deviation weighted by a bias factor would accomplish the desired objective.

This control signal is known as area control error (ACE).ACE serves to

indicate when total generation must be raised or lowered in a control area.

In an interconnection, there are many control areas, each of which performs

its AGC with the objective of maintaining the magnitude of ACE (area Control

Error) “sufficiently close to 0” using various criteria. In order to maintain the

frequency sufficiently close to its synchronous value over the entire

interconnection, the coordination of the control areas’ actions is required. As

each control area shares in the responsibility for load frequency control,

effective means are needed for monitoring and assessing each area’s

performance of its appropriate share in load frequency control.



OBJECTIVE

The objective of the controller is to generate and delivered power in an

interconnected system as economically and reliably as possible while

maintaining the voltage and frequency within permissible limits.

FUZZY LOGIC

The past few years have witnessed a rapid growth in the number and variety

of applications of fuzzy logic (FL). FL techniques have been used in image-

understanding applications such as detection of edges, feature extraction,

classification, and clustering. Fuzzy logic poses the ability to mimic the

human mind to effectively employ modes of reasoning that are approximate

rather than exact. In traditional hard computing, decisions or actions are

based on precision, certainty, and vigor. Precision and certainty carry a cost.

In soft computing, tolerance andimpression are explored in decision making.

The exploration of the tolerance for imprecision and uncertainty underlies

the remarkable human ability to understand distorted speech, decipher

sloppy handwriting, comprehend nuances of natural language, summarize

text, and recognize and classify images. With FL, we can specify mapping

rules in terms of words rather than numbers. Computing with the words

explores imprecision and tolerance. Another basic concept in



FL is the fuzzy if–then rule. Although rule-based systems have a long history

of use in artificial

intelligence, what is missing in such systems is machinery for dealing with

fuzzy consequents or

fuzzy antecedents. In most applications, an FL solution is a translation of a

human solution.

Thirdly, FL can model nonlinear functions of arbitrary complexity to a desired

degree of accuracy.FL is a convenient way to map an input space to an

output space. FL is one of the tools used to model a multi-input,multi-output

system.

III. FUZZY LOGIC CONTROLLERS



The design of Fuzzy Logic Controller can be divided into three areas namely, the allocation of

the areas of inputs, the determination of the rulesassociated with the inputs and outputs and the

defuzzification of the output into a real value.

A fuzzy inference system (FIS) essentially defines a nonlinear mapping of the

input data vector into a scalar output, using fuzzy rules. The mapping

process involves input/output membership

functions, FL operators, fuzzy if–then rules, aggregation of output sets, and

defuzzification.

An FIS with multiple outputs can be considered as a collection of

independent multi-input, single-output systems. A general model of a fuzzy

inference system (FIS) is shown above. The FLS maps crisp inputs into crisp

outputs. It can be seen from the figure that the FIS contains four

components: the fuzzifier, inference engine, rule base, and defuzzifier. The

rule base contains linguistic rules that are provided by experts. It is also

possible to extract rules from numeric data. Once the rules have been

established, the FIS can be viewed as a system that maps an input vector to

an output vector. The fuzzifier maps input numbers into corresponding fuzzy



memberships. This is required in order to activate rules that are in terms of

linguistic variables.

The fuzzifier takes input values and determines the degree to which they

belong to each of the fuzzy sets via membership functions. The inference

engine defines mapping from input fuzzy sets into output fuzzy sets. It

determines the degree to which the antecedent is satisfied for each rule.

If the antecedent of a given rule has more than one clause, fuzzy operators

are applied to obtain one number that represents the result of the

antecedent for that rule. It is possible that one or more rules may fire at the

same time. Outputs for all rules are then aggregated. During aggregation,

fuzzy sets that represent the output of each rule are combined into a single

fuzzy set.

Fuzzy rules are fired in parallel, which is one of the important aspects of an

FIS. In an FIS, theorder in which rules are fired does not affect the output.

The defuzzifier maps output fuzzy sets into a crisp number. Given a fuzzy set

that encompasses a range of output values, the defuzzifier returns one

number, thereby moving from a fuzzy set to a crisp number. Several

methods for defuzzification are used in practice, including the centroid,

maximum, mean of maxima, height, and modified height defuzzifier. The

most popular defuzzification method is the centroid, which calculates and

returns the center of gravity of the aggregated fuzzy set. FISs employ rules.

However, unlike rules in conventional expert systems, a fuzzy rule localizes a

region of space along the function surface instead of isolating a point on the

surface. For a given input, more than one rule may fire. Also, in an FIS,

multiple regions are combined in the output space to produce a composite

region.

The AGC based on FLC is proposed in this study. One of its main advantages

is that controller parameters can be changed very quickly by the system

dynamics because no parameter estimation is required in designing

controller for nonlinear systems. Therefore a FLC, which representsa model-

free type of nonlinear control algorithms, could be a reasonable solution.



There are many possibilities to apply fuzzy logic to the control system. A

fuzzy system knowledge base consists of fuzzy IF-THEN rules and

membership functions characterizing the fuzzy sets .The result of the

inference process is an output represented by a fuzzy set, but the output of

the fuzzy system should be a numeric value. The transformation of a fuzzy

set into a numeric value is called defuzzification.

In addition, input and output scaling factors are needed to modify the

universe of discourse. Their role is to tune the fuzzy controller to obtain the

desired dynamic properties of the process-controller closed loop .In this

paper, the inputs of the proposed Fuzzy

controllers are area control error (ACE), and change ratein area control error

(ACE) as shown in Figure, whichis indeed error (e) and the derivation of the

error(⋅e) ofthe system, respectively. This gives us a fairly good indicator of

the general tendency of the error.According to the conventional automatic

control theory, the performance of the PI controller is determined by its

proportional parameter and integral parameter. The proportional term

provides control actionequal to some multiple of the error, while the integral

term forces the steady state error to zero. Whenever the steady-state error

of the control system is eliminated, it can be imagined substituting the input

ACE of the fuzzy controller with the integration of error. This will result in the

fuzzy controller behaving like a parameter timevaryingPI controller; thus the

steady-state error isremoved by the integration action. However,

thesemethods will be hard to apply in practice because of the difficulty of

constructing fuzzy control rules. Usually, fuzzy control rules are constructed

by summarizing the manual control experiences of an operator. The operator

intuitively regulates the executer to control the process by watching the

error and the change rate of the error between output of the system and the

set-point value given by the technical requirement. It is no practical wayfor

the operator to observe the integration of the error of the system. Therefore

it is impossible to explicitly abstract fuzzy control rules from the operator’s

experience. Hence, it is better to design a fuzzy controller that possesses the



fine characteristics of the PI controller by using only ACE and ACE. One way

is to have an integrator serially connected to the output of the fuzzy

controller, as shown in Fig. 3 . The control input to the plant can be

approximated by

uβ∫utdt(3)

Where β is the integral constant, or output scaling factor. Hence, the fuzzy

controller becomes a parameter timevarying PI controller. The controller is

called as PI–type fuzzy controller, and the fuzzy controller without the

integrator as the PD–type fuzzy controller. In a PI–type fuzzy control system,

the steady-state error is zero, but when the integral factor is small the

response of the system is slow, and when it is too large there is a high

overshoot and serious oscillation term forces the steady state error to zero.

Whenever thesteady-state error of the control system is eliminated, itcan be

imagined substituting the input ACE of the fuzzy controller with the

integration of error. This will result inthe fuzzy controller behaving like a

parameter time varying PI controller; thus the steady-state error is removed

by the integration action.

However, these methods will be hard to apply in practice because of the

difficulty of constructing fuzzy control rules. Usually, fuzzy control rules are

constructed by summarizing the manual control experiences of an operator.

The operator intuitively regulates the executer to control the process by

watching the error and the change rate of the error between output of the

system and the set-point value given by the technical requirement. It is no

practical wayfor the operator to observe the integration of the error of the

system. Therefore it is impossible to explicitly abstract fuzzy control rules

from the operator’s experience. Hence, it is better to design a fuzzy

controller that possesses the fine characteristics of the PI controller by using

only ACE and ACE.



One way is to have an integrator serially connected to the output of the

fuzzy controller, as shown in Fig below. The control input to the plant can

beapproximated by u β∫utdt

where βis the integral constant, or output scaling factor. Hence, the fuzzy

controller becomes a parameter timevaryingPI controller. The controller is

called as PI–type fuzzy controller, and the fuzzy controller without the

integrator as the PD–type fuzzy controller. In a PI–type fuzzy control system,

the steady-state error is zero, but when the integral factor is small the

response of the system is slow, and when it is too large there is a high

overshoot and serious oscillation.

The type of the FLC obtained is called Mamdani -type which has fuzzy rules

of the form If ACE is Ai and ACE is Bi THEN u is Ci i=1,…, n. Here, Ai, Bi, Ci,

are the fuzzy sets. The triangle membership functions for each fuzzy

linguistic values ofthe ACE and ACE are shown in Table 2 [8], in which NB,

NM, NS, Z, PS, PM, and PB represent negative big, negative medium,

negative small, zero, positive small, positive medium, and positive big,

respectively.



Fuzzy rule base with 7 membership function

4.6 System under study with FLC

FLC is designed for the system using the “Fuzzy Logic Tool Box” in MATLAB. The fuzzy logic

tools for building, editing and observing fuzzy inference systems: Fuzzy Inference System (FIS)

Editor, Membership Function (MF) Editor, Rule Editor, Rule Viewer and Surface Viewer. In the

FIS editor, the input variables are named as ACE and INTACE and the output variable as

ACEOUT and their ranges are specified. In the MF editor the type of MF associated with each

variable is defined. In the rule editor rules are framed which define the behavior of the system.

The FLC designed is shown in fig. 4.5.The FLC is designed for the system under study and it

replaces the conventional integral controller in both areas. Before simulating the model in

MATLAB, the FLC (is. 515 file) is saved in the workspace with some name that is further used

in the AGC model.4.7 DYNAMIC Responses. The dynamic responses of two equal area reheat



thermal system without GRC, following a 1% step load perturbations in 136 area I are shown in

fig. below,



Application of Fuzzy Logic to Automatic Generation Control

Fuzzy logic is used to calculate ACE (out) i.e. control signal in the form of

area control error that will beprovided to both the areas to generate

according to change in total load to maintain the system frequencywithin

permissible limits. Area control error and change in frequency of the system

as input are used asinputs for FLC.

Algorithm for fuzzy logic application to AGC problem

The calculation of the control action in the fuzzy algorithm consists of

following four steps.

1. Calculate area control error (ACE) and change of frequency (delF).

2. Convert the error and change of frequency into fuzzy variables i.e.

linguistic variables such as Positive Big (PB), Positive Medium (PM) etc., as

given below.

3. Evaluate the decision rules shown in rule base given below using the

compositional rule of inference.

4. Calculate the deterministic input required to regulate the process.The

control rules are formulated in linguistic terms using fuzzy sets to describe

the magnitude of error, the frequency deviation and themagnitude of the

appropriate control action.

NS = negative small

ZE = zero

PS = positive small

Allocation of Areas of Inputs and Outputs

The inputs to FLC are taken as ACE and ∫ACE the the previous chapters, with the conventional

integral controller the responses for ACE and ∫ACE are plotted as shown in fig.3.5 and 3.6 .By

examining these responses, the overall maximum and minimum values are noted. It can be seen



from figure that the overall maximum is + 0.003 and overall minimum is — 0.022. However, for

Fuzzification tolerance is kept for maximum and minimum values. Hence the logical control

range is considered as -0.05 and +0.05. This range has been divided into control areas by

membership functions that are triangular or trapezoidal. The input output ranges are as shown

Input range for ACE and INTACE with 3 MF’s: N [-0.09 -0.055 -0.044 -0.01]; Z [ -0.025 0

025 ]P [0.01 0.04 0.05 0.1]Output range for ACE OUT ( -0.02 to 0.02 ) with 3 MF’s: N [-0.03 -

002 -0.0180 -0.006]Z [-0.013 0 0.009]P[4-0.005 0.018 0.02 003] Fig 4.2, 4.3 and 4.4 show the

Membership Functions with ranges for input and output variables.



FUZZY RULE BASE

(With three membership function)

The rules are developed heuristically for a particular task and are implemented as a set of fuzzy

conditional statements. The rules used are obtained by examining the output response to

corresponding inputs to the fuzzy controller. The rules used for the design of fuzzy logic

controller are described in the fuzzy rule table shown below: ACE OUT, ACE

The interpretation of rules is done as follows:

If ACE is N and INTACE is N then ACEOUT is N. If ACE is N and INTACE is Z then

ACEOUT is N.



AUTOMATIC GENERATION CONTROL

Performance of AGC under normal and abnormal conditions

Under normal conditions with each area able to carry out its control

obligations, steady state corrective action of AGC is confined to the area

where the deficit or excess of generation occurs. Inter area power transfers

are maintained at scheduled levels and system frequency is held constant.

Under abnormal conditions, one or more areas may be able to correct for the

generation-load mismatch due to insufficient generation reserve on AGC. In

such an event, other areas assist by permitting the inter areas power

transfers to deviate from scheduled values and by allowing system

frequency to depart from its pre disturbance value. Each area participates in

frequency regulation in proportion to its available regulating capacity relative

to that of overall system.

MODELLING OF LOAD FREQUENCY CONTROL



AGS is used in real time control to match the generation with demand .The

rising and falling of demand,alters the system voltage and frequency. Real

power and reactive power is changed due to change in frequency and

voltage. AVR controls the reactive power and voltage.LFC controls real power

and frequency. AGC tracks system load and generation level of each

committed unit. Modern power system network consists of a number of

utilities interconnected together and power isexchanged between utilities

over tie-lines by which they are connected. Automatic generation control

(AGC) plays a very important role in power system, its main role is to

maintain the system frequency andtie line flow at their scheduled values

during normal period. During large transient disturbance andemergencies

AGC is by passed and other emergency control takes over. The

synchronization of differentsystem to interconnected system depends upon

(I) voltage magnitude (2) frequency and (3) phasesequence. Any wide

deviation from the nominal value of frequency or voltage will lead the

system to total collapse. Hence AGC has gained importance with the growth

of interconnected systems and with rise in size of interconnected system

automation of the control system have aroused. A number of control

strategies exist to achieve better performance.

Due to non-linearities of power system, system parameters are linearized

around an operating point. PI controller is generally used. The disadvantage

of PI controller is that the mathematical model of the control process may

not exist or may be too expensive in terms of computer processing powers

and memory. Using FLC, applying to LFC problems, gives a better

performance.

Power system operators have the end responsibility to ensure that adequate power is delivered to

the load reliably and economically. In order to ensure this electrical energy system must be

maintained at desired operating state represented by nominal frequency, voltage profile and load

flow configuration. This is made possible by having a close control of real and reactive power

generations of the system. The real and reactive power demands on the power system are never

steady, but continuously vary with the rising or falling trend. The real and reactive power



generations must change accordingly to match the load perturbations. The control of an electrical

energy system, In order to have an exact matching of the generation to load at nominal state, is

quite a challenging problem. It is so because in a dynamic system the load continuously changes

and system generation, responding to control impulses, chases the load with the transient

unbalance of load and generation reflected in speed hence frequency variations. The variations

should be with in the tolerance levels. For small perturbations, the active power is dependant on

internal machine angle δ and is independent of bus voltage, while bus voltage is dependant on

machine excitation and is independent of machine angle δ. Change in angle δ is caused by

momentary change in generator speed. Therefore, load frequency and excitation voltage control

problems are non interactive for small perturbations hence treated as two independent ‘decoupled

control problems for all practical purposes. In any power system, it is a desirable feature to

achieve a better frequency consistency than is obtained by a speed governing system alone.

In the large interconnected power system, it is also desirable to maintain the tie-line power flow

at a given level irrespective of load changes in any area. To accomplish this, it becomes

necessary to automatically regulate the operations of main steam valves or hydro gates in

accordance with a suitable control strategy, which in turn controls the real power output of

electric generators. The problem of controlling the output of electric generators in this way is

termed as Automatic Generation Control (AGC).The AGC problem of a large interconnected

power system is studied by dividing the entire power system into a number of control areas. A

“control area” is defined as a power system, a part of the system or a combination of the system

to which a common generation control scheme is applied and the frequency is assumed to be the

same through out, in static as well as dynamic conditions. All the generators in the control area

swing in unison and form a coherent group. Automatic Generation Control (AGC) of an

interconnected system is defined as “The regulation of power output of generators within a

prescribed area, in response to changes in system frequency, tie-line power, or the relation of

these to each other, so as to maintain scheduled system frequency and/or the established power

interchanges with other areas within the prescribed limits”.

The main objectives of AGC would be as follows:

i) Each control area should take care of its own load demand.

ii) The tie-line flows should be scheduled as per the system economics.

iii) The system frequency should be maintained as far as possible isochronously



iv) In emergency, neighboring areas should help each other based on system economics.

The present study considers the following qualitative specifications for design purpose according

to Elgerd and Fosha.

1. The steady state frequency error following a step load change in an area should be zero.

2. The steady state change in tie-line power following a step load change in an area should be zero.

3. The area control error should be zero.

4. The transient frequency and tie line power errors should be small. The problem of Automatic

Generation Control can be sub divided into fast (primary) and slow (secondary) control modes.

The loop dynamics following immediately upon the onset of the load disturbance is decided by

fast primary mode of AGC. This fast primary mode of AGC is also known as “Uncontrolled

mode” since the speed changer position is unchanged. The secondary control acting through

speed changer and initiated by suitable controller constitutes the slow secondary or the

“Controlled modes” of AGC. The overall performance of AGC in any power system depends on

the proper design of both primary and secondary control loops. Literature survey shows that a lot

of work pertaining to secondary control aspect of AGC has been reported. Secondary controllers

are designed to regulate the area control errors to zero effectively. Many investigations in the

area of AGC of interconnected power systems have been reported over the past six decades. The

amount of literature in the area of Automatic Generation Control (AGC) is very vast. Some of

the works have been reviewed here. Most of the research work in AGC deals with “net

interchange tie line bias control” strategy making use of Area Control Errors (ACE), which

reflects mismatch of generation and load in a control area. Area supplementary control would

change generation in such a manner as to keep the ACE to a minimum. It would not be desirable

even if it were possible, to maintain ACE at zero because this would require unnecessary rapid

maneuverings of generator unit. Elgerd and Fosha have optimized the gain setting of integral

controllers using integral of squared error (ISE) technique considering several values of

frequency bias B for a two equal area non reheat thermal system. Their investigations reveal that

the best dynamic performance is obtained for B=0.5β. However, Cohn, Ross and others have

focused in their technical publications that B less than β is not acceptable. The investigation of

Concordia and Kirchmayer on simulation studies of AGC of two equal area thermal systems

shows that for minimum interaction between control areas, frequency bias must be set equal to

area frequency response β. They have also analyzed the effect of governor dead band on system



dynamic responses. Nanda and Kaul have extensively studied the AGC problem of two equal

area reheat thermal system using both parameter plane for optimization of integral gain setting

and for investigation of the degree o f stability of the system. They have analyzed the effect of

Generator Rate Constraint (CRC), speed regulation parameter R in the optimum controller

setting and system dynamic performance.

The Advantages:

– Limits the variations.

– Avoid machine damages

– Avoid blackouts

– Enhance the system reliability and security.



Conclusion

Automatic Generation control problem of a large interconnected power system has been studied

by dividing the whole system into a number of control areas. The standard control strategy used

in the industry to meet the requirement of Zero The system dynamic responses have been found

by simulating the models in MATLAB. The following table gives a comparison of various

modes of operation of two equal area systems. The uncontrolled mode of operation of the two

equal area systems is discussed first. It is observed that the responses have a steady state error,

which violates the control strategy. Hence, the necessity of having a controller for AGC is

emphasized. The two equal area reheat thermal system with Integral controller is modeled and

the dynamic responses are observed. The integral controller eliminates the steady state errors in

frequency and tie line power. The maximum peak deviations in frequency, tie line power and

area control error are found to be -0.022, -0.006, -0.015 respectively. All the responses have

settled at around 20 sec. The controlled mode of AGC with Generation Rate Constraint (GRC) is

also discussed in this work. The dynamic responses are found to be deteriorating. In the next part

of the work, the conventional integral controller is replaced by the fuzzy logic controller (FLC)

and the dynamic responses are observed after simulation. The FLC is designed with ACE and ∫

ACE as the inputs. Three membership functions are considered for input and output variables. It

is observed from the responses that with the FLC the oscillations in the positive side are totally

eliminated in case of area control error and tie- line power and they are almost eliminated in case

of frequency. The maximum peak deviations in the responses remained unaffected. The settling



time is also observed to be the same i.e. around 20 sec. It is observed that the Fuzzy Logic

Controller provides the oscillations of smaller magnitude compared to the conventional ones.

Hence Fuzzy Logic Controller is proved to be effective in Automatic Generation Control of two

equal area reheat thermal system. In this work an attempt is made to compare the performance of

the Fuzzy Logic Controller with Integral Controller. Further work can be done in designing the

Fuzzy controller with 5 and 7 membership functions. But as the membership functions increase,

the number of rules increases and the design becomes complex. Fuzzy controller can be designed

to improve the dynamic performance of the two area system is included. Variable structure

controller, which is a combination of conventional and fuzzy control, can also be designed to

study the problem of AGC with and without GRC.

REFERENCES

[1] Chun-fengLu,Chun-Chang Liu ,ChiJui Wu, Effect of Battery Energy Storage

System on Load Frequency Control Considering Governor Deadband and

Generation Rate Constraints, IEEE Trans.Power Sys.,10(3),1995

[2] J.K Cavin, M.C. Budge, P. Rosmunsen, An Optimal Linear System Approach

to Load-Frequency Control, IEEE Trans.on PAS-90, 1971

[3] Z.M. Ai-Homouz and Y.L. Abdel-Magid , Variable Structure Load Frequency

Controllers for Multiarea Power Systems, Int.J. Electr. Power Energy System

15(5),1993

[4] C.S.Chang, Weihui Fu, Area Load Frequency Control Using Fuzzy Gain

Scheduling of PI Controllers, Electric Power Systems Research, 42,1997

pp.145-152

[5] BarjeevTyagi and S. C. Srivastava “A Decentralized Automatic Generation

Control Scheme for Competitive Electricity Markets”.IEEE transactions on

power systems, vol. 21, no. 1, pp 312-320, February 2006.



[6]Bjorn H. Bakken and Ove S. Grande, Automatic Generation Control in a

Deregulated Power System , IEEE Transactions on Power Systems, Vol. 13,

No. 4,pp 1401 1406, November 1998.

[7] D.M. Vinod Kumar, “Intelligent controllers for Automatic Generation

Control”.IEEE, pp 557-574, 1998.

[8]EnginYesil, AysenDemiroren, ErkinYesil “Automatic generation control with

fuzzy logic controller in the power system including three areas”

[9] G.A. Chown and R. C. Hartman, “Design and Experience with a Fuzzy

Logic Controller for Automatic Generation Control (AGC)” pp352-357, 1997

[10] G. L. Kusic, J.A. Sutterfield, A. R. Caprez, J. L. Haneline, B. R. Bergman

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Seminar Final Report