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CHAPTER 2

Literature Review of Load Frequency Control

2.1 Introduction

There are volumes of research articles which have been appeared in the literature

regarding Automatic Generation Control (AGC)/LFC of single area/multi area power

system considering various control strategies. These are summarized comprehensively in

[IBR 06, KUM 96]. This section, specifically gives a brief literature review on LFC of

power system using intelligent control techniques. A number of investigations have been

done in the area of LFC [JAL 92, KUM 96]. The successful operation of interconnected

power systems requires the matching of total generation with total load demand and

associated system losses [MIL 71, NAN 78]. With time, the operating point of a power

system changes, and hence, these systems may experience deviations in nominal system

frequency and scheduled power exchanges to other areas, which may yield undesirable

effects [CON 54, MIL 71].

There are two variables of interest, namely, frequency and tie-line power exchanges

[FOS 70, HAN 02]. Their variations are weighted together by a linear combination to a

single variable called the Area Control Error (ACE) [KWA 75, NAG 05]. The AGC

problem has been augmented with the valuable research contributions from time to time,

like AGC regulator designs incorporating parameter variations/uncertainties, load

characteristics, excitation control, and parallel ac/dc transmission links [MAT 07]. The

microprocessor based AGC regulator, self-tuning regulator, and adaptive AGC regulator

designs have also been presented [MOO 72, PAN 89]. The small signal analysis is justified

for studying the system response for small perturbations. One important factor for reliable

supply is dependent on matching the electricity generation to the load demand and this

factor is dependent on number of power plants. There are different types of power plants

which supply reliable and good quality of electricity to their consumers. Power plants are

dependent on type of energy source. So the researchers are investigating different types of

power plant. Power plants can be thermal, hydro, wind, solar, nuclear and some other type

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source of electricity generation [HAS 12, KAZ 02]. Researchers incorporated different

types of nonlinearities as in actual system to demonstrate the real problem in the generating

plant [GRE 96, TRI 84].

2.2 Approaches to realistic systems

However, the implementation of AGC strategy based on a linearized model on an

essentially nonlinear system does not necessarily ensure the stability of the system.

Considerable attention has been paid by researchers to consider the system nonlinearities to

develop a robust controller. It is shown in the literature that governor dead-band

nonlinearity tends to produce continuous oscillations in the area frequency and tie-line

power transient response which produces destabilizing effect on the system [TRI 84].

The first attempt in the area of AGC has been to control the frequency of a power

system via the fly wheel governor of the synchronous machine [KUM 98]. This technique

was subsequently found to be insufficient, and a supplementary control was included to the

governor with the help of a signal directly proportional to the frequency deviation plus its

integral [LIA 93, LIM 96, MIL 71, MOO 72, NAG 05]. This scheme constitutes the

classical approach to the AGC of power systems. Very early works in this important area

of AGC have been by Cohnetal [KUM 96]. This work was based on tie-line bias control

strategy. Quazza [QUA 77] illustrated non interactive control considering:

1. Non-interaction between frequency and tie-line powers controls

2. Each control area taking care of its own load variations.

Supplementary controllers were designed to regulate the ACE to zero effectively

[CHA 05, COH 57]. Later on, energy source dynamics were incorporated in AGC

regulator design [MIL 71, NAN 04]. The standard definitions of the terms associated with

the AGC of power systems were finalized in [IEEE 92]. Following that, suggestions for

dynamic modeling for LFC are discussed thoroughly in [KUN 04]. Based on the

experiences with actual implementation of AGC schemes, modifications to the definition

of ACE are suggested from time to time to cope with the changed power system

environment [GEG 95]. Since many presently regulated markets are likely to evolve in to a

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hybrid scheme, and some deregulated markets are already of this type, the effects of

deregulation of the power industry on LFC have been addressed thoroughly [CHO 98,

NAN 04, SAB 08, SHE 84].

2.2.1 Incorporating system nonlinearities

Later on, the effect of Generation rate constraint (GRC) was included in these types

of studies, considering both continuous and discrete power system models [SCH 96].

Incorporating the dynamics of the energy source in AGC regulator design, Kwatny et.al

[KWA 75] have proposed an optimal tracking approach to AGC, considering load to be the

output of the dynamic system. The small signal analysis is justified for studying the system

response for small perturbations [GRE 96]. However, the implementation of AGC strategy

based on a linearized model on an essentially nonlinear system does not necessarily ensure

the stability of the system [KUN 07]. Considerable attention has been paid by researchers

to consider the system nonlinearities [CHI 05, DAS 91]. Destabilizing effect of governor

dead-band nonlinearity on conventional the AGC system shown in [IBR 04]. It is shown

that governor dead-band nonlinearity tends to produce continuous oscillations in the area

frequency and tie-line power transient response [YOU 00].

2.3 Control techniques

A lot of control techniques are proposed by the researches in there pioneer work to

design LFC controllers. The controllers are based on:

Classical control techniques

LQR based controlling techniques

Proportional, Derivative, Integral controlling techniques

Soft computing techniques/Artificial intelligence (AI) techniques

Fuzzy logic based techniques

Neural network based techniques

Genetic Algorithm based techniques

Particle Swarm based techniques

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Other hybrid techniques

Description of load frequency control techniques are described by different researchers.

2.3.1 Classical control techniques

The pioneering work by a number of control engineers, namely Bode, Nyquist, and

Black, has established links between the frequency response of a control system and its

closed-loop transient performance in the time domain [OGA 02]. The investigations

carried out using classical control approaches reveal that it will result in relatively large

overshoots and transient frequency deviation [CHA 05, KUN 04, MOM 08, WOO 96].

Moreover, the settling time of the system frequency deviation is comparatively long and is

of the order of 10–20s [VIN 98].The AGC regulator design techniques using modern

optimal control theory enable the power engineers to design an optimal control system with

respect to given performance criterion [PAN 89]. Fosha and Elgerd [FOSA 70] were the

first to present their pioneering work on optimal AGC regulator design using this concept.

A two area interconnected power system consisting of two identical power plants of

nonreheat thermal turbines was considered for investigations [CON 54, QUA 77].

Adaptive control is a control methodology of controller to adapt with parameter

variations. Adaptive control strategy is found to be very effective in improving the stability

and transient response of the system. There is a difference between robust control and

adaptive control technique. Robust control technique guarantees that control laws for

parameter variations can‟t change for a given boundary. While adaptive control law will be

change with changing conditions. Ross proposed an error adaptive computer control

(EACC) technique which gave new and different degrees of freedom for automatic control.

It is a logical computer technique which monitors the control error signal and makes

logical decisions on how much, if any, control action should be taken depending on the

characteristics of the error signal [ROS 66]. Control action may be reduced to any

judiciously chosen minimum by adjusting the EACC to discriminate against certain classes

of disturbance, e.g., statistical, deterministic, and periodic. Fast control action may still be

taken for large and/or sustained disturbances. Tripathi et.al proposed an adaptive regulator

which is implemented on a microprocessor. They suggested that application of self-tuning

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control for load-frequency regulators is suitable for alternative integral control, for load-

frequency control [TRI 84]. Vaj et.al presents an adaptive regulator which uses the a priori

known information and satisfies the multi-objective character of the control of the

Hungarian power system [VAJ 85]. Pan et.al uses a PI adaptation to satisfy the hyper

stability condition for taking care of the parameter changes of the system. Only the

available information on the states and output are required for the control [PAN 89]. Sho

et.al proposed a multiarea adaptive load frequency control (LFC) based on the self-tuning

regulator (STR) for a comprehensive automatic generator control simulator (AGCS) [SHO

93]. Liaw et.al proposed controller uses a signal synthesis adaptation approach such that

the performance of the controlled system is insensitive to the changing load and system

parameter variations [LIA 94]. It is designed such that only the outputs of the controlled

plant are fed back to the controller. A reduced reference model is used to simplify the

design, making the controller easier to implement. Self tuning controllers are also a part of

adaptive control scheme [SHE 84].

The self tuning controllers are optimized the running parameter of the system for

fulfillment of objective function which could be maximized or minimized [YAM 89].

Generally the error is minimized and the efficiency is maximized. Lee et.al used a control

methodology where extended least squares technique is used for estimation of parameters

[LEE 91]. The area generator corrective control is computed by a self-tuning algorithm

derived from the minimization of a generalized cost function, which is defined in terms of

the system output and the weighting on the control effort.

2.3.1.1 LQR based controlling techniques

Optimal control design for the linear systems with quadratic performance (Linear

Quadratic Regulator (LQR) has been established [IBR 09, JHO 96, KAK 09, RAY 01].

The purpose of optimal regulator design is to determine the optimal control rule. There are

several competing objectives that need to be simultaneously satisfied (system step

response, rise time, overshoot, disturbance rejection, or integral absolute error). These

objectives are imbedded in system‟s Eigen values that are measures of system stability and

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robustness. [Ray 09] proposed a combination of „Matching conditions‟ and Lyapunov

stability theory is adopted to implement a robust stabilizing controller.

Yang et.al suggested a controller which is based on structured singular values

(SSVs) and each local area load-frequency controller can be designed independently [YAN

96, YAN 02]. The robust stability condition for the overall system can be easily stated to

achieve a sufficient interaction margin and a sufficient gain and phase margin defined in

classical feedback theory during each independent design.

Ko et.al published that LQR problem needs to be reformulated for finding a

common Lyapunov function for the set of considered linear systems [KO 08]. This is

accomplished by representing the underlying control optimization problem in terms of a

system of linear-matrix-inequality (LMI) constraints. The solution of LMI equations

involves a form of quadratic Lyapunov function that not only gives the stability property of

the controlled system but can also be used for achieving certain performance specifications

[WAN 93].

The H∞ control strategy was used many scientists and researchers. The advantage of

H∞ control technique is to use this technique in multi variable system and also where cross

compiling between the channel exits [AL 07, RAH 98]. Although the H∞ control problem

can be regarded as robustness against exogenous signal uncertainty, in the case when

parameter uncertainty appears in the plant modeling, robust behavior on H∞ performance

as well as stability cannot be guaranteed by standard H∞ control. The necessary and

sufficient conditions for the existence of robust H∞ controller design with circular pole

constraints are derived in terms of a linear matrix inequality (LMI) in order to contruct a

desired controller. Kan et.al proposed a H∞ load-frequency control for interconnected

power systems with circular pole constraints [KAN 05]. In their study of robust H∞ control

problem whose the objective is to design controllers such that the closed-loop system is

stable and the H∞ norm of a specified closed-loop transfer function is minimized. Cimen

et.al designed a robust load frequency controller based on H∞ design techniques applied

MIMO system [CIM 00]. Al-Tamirni proposed that H∞ load-frequency controller for a

power system generator will be designed without the knowledge of the system model. The

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idea is to solve for an action dependent value function of the zero-sum game instead of

solving for the state dependent value function which satisfies a corresponding game

algebraic Riccati equation (GARE). Mandour et.al proposed an algorithm was the H∞

control using γ iteration. This algorithm will use the loop shifting two Riccati formula of

H∞ control [MAN 10].The disadvantage of H∞ approach is that this technique includes

higher level of mathematical analysis and system nonlinearity cannot be well handled.

2.3.1.2 Proportional, Derivative, Integral controlling techniques

Among various types of load frequency controller, the PI controller is most widely

used to speed-governing system for LFC scheme [CHA 07, JHO 96, NAN 04]. An

advantage of the PI control technique is to reduce the steady-state error to zero by feeding

the errors in the past forward to the plant [CHA 98, PAT 90, SHO 93].

The most of proposed techniques were based on the classical proportional and

integral (PI) or proportional, integral and derivative (PID) controllers. Its use is not only

for their simplicities, but also due to its success in a large number of industrial applications

[LIM 96, SAB 08, VIN 98]. For PID controller design, overshoot/undershoot and settling

time are used as objective function for multi-objective optimization in LFC problem [KAR

02]. One of the developments in the field of modern control theory is in the direction of its

application in optimal control allows the power engineers to cope with the problems arising

out of due to the complex structure of power systems [SON 97]. The development of

design techniques for load frequency control of a power system in the last few years is very

significant.

AGC regulator designs are based on adaptive control schemes [RUB 94]. The AGC

regulator design techniques using modern control system theory enable the power

engineers to design the optimal control system with respect to given performance criteria.

Most of the researchers have taken the dynamic system equations for the development of

adaptive controller, to cope up the change in process parameters and unmodelled process

dynamics [TAL 99]. Many researchers have carried out research work on adaptive

automatic generation control scheme which has been reported in the literature. The main

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contribution of the proposed controller is to enhance the controlled performance of the

conventional PID controller by adding a self-tuning on the existing conventional PID

controller. The conventional PID controller with online self-tuning pre compensation

shows superior performance than conventional PID controller [PAN 89].

The emerging techniques of artificial intelligence (AI) have a common feature, i.e.

they had the ability to process the complex information [CHI 96, GAH 94, HOL 75, RER

03]. In AI technique all the short circuit analysis in three-phase is carried out offline, and

the fault is located online within short time. Different AI tools were successfully employed

for all power system purposes such as Expert Systems, Artificial Neural Network (ANN),

Fuzzy Logic and genetic algorithm realizing distinctive performances over the

conventional ones [CHA 09, DAS 03, KAZ 02]. Among the various AI based techniques,

fuzzy logic approach is observed to be applicable and attractive for dealing with complex

and ill-defined problems which may be impossible or too expensive with conventional

methods [CHA 07, CHA 98, CHO 98].

2.3.2 Soft computing techniques/Artificial intelligence (AI) techniques

The emerging techniques of artificial intelligence (AI) have a common feature, i.e.

they had the ability to process the complex information. In AI technique all the short

circuit analysis in three-phase is carried out offline, and the fault is located online within

short time. Different AI tools were successfully applied for all power system purposes such

as Expert Systems, Artificial Neural Network (ANN), Fuzzy Logic and genetic algorithm

realizing distinctive performances over the conventional ones [ZEY 02]. Among the

various AI based techniques, fuzzy logic approach is observed to be applicable and

attractive for dealing with complex and ill-defined problems which may be impossible or

too expensive with conventional methods.

2.3.2.1 Fuzzy logic based techniques

A fuzzy logic based intelligent controller is designed to facilitate the smooth

operation and less oscillatory when system is subjected to a sudden load change [ANA 09,

ARA 09, GEG 95]. Fuzzy controller is based on a logical system called fuzzy logic which

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is much closer in spirit to human thinking and natural language than classical logical

systems [CHA 07, HA 98, IND 95, SIJ 02, XIU 06]. Fuzzy set theory and fuzzy logic

establish the rules of a nonlinear mapping [CHA 09]. The main goal of LFC in

interconnected power systems is to protect the balance between production and

consumption [EI 08, ISM 92]. Because of the complexity and multi-variable conditions of

the power system, conventional control methods may not give satisfactory solutions [MAT

06, MOO 01]. On the other hand, their robustness and reliability make fuzzy controllers

useful in solving a wide range of control problems [MEN 03, NAN 04, TAL 99]. Load

frequency control in two area system using fuzzy logic algorithm is found to be suitable

[SON 97, SON 98, XIU 06]. But the fix fuzzy rule expert systems have some drawbacks as

[CHA 07]:

1. It is difficult to acquire knowledge

2. There is no adaptability and hence for dynamic time varying system, it is unable to

perform well due to change in system.

To overcome these drawbacks, many researchers used adaptive technique with

fuzzy logic to control the dynamics. Talaq proposed an adaptive fuzzy gain scheduling

scheme for conventional PI and optimal load frequency controllers. Comparison

between fixed gain and adaptive fuzzy gain controllers were shown. In this controller a

sugeno type fuzzy inference system is proposed [TAL 99]. This controller performs better

for linear and nonlinear system both. They also discussed that adaptive fuzzy control

methodology also takes lesser operating time because here lesser need of training patterns.

Trabelsi et.al proposed an adaptive T-S fuzzy logic controller where fuzzy clustering is

based for fuzzy clustering [TRA 04]. Osyal et.al proposed a “Dynamical Fuzzy Network

(DFN)” for LFC. A DFN is that which contains dynamical elements such as delayers or

integrators in their processing units is used in the adaptive controller design for load

frequency control [OYS 05]. Adaptation is based on the DFN training that is adjusting

parameters of DFN for load frequency control in power systems with Broyden-Fletcher-

Golfarb-Shanno (BFGS) gradient algorithm. This is done by minimizing the cost

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functional of load frequency errors. The cost gradients with respect to the network

parameters are calculated by adjoint sensitivity.

EI-Metwelly presented an adaptive fuzzy logic control approach for designing a

decentralized controller for load frequency control of interconnected power areas [EI 08].

The T-S fuzzy-logic configuration is adapted to implement the FLFC. For adaptation EI-

Metwelly choose the variable structure algorithm to implement our estimator since it leads

to a robust performance and an efficient numerical implementation. Pra et.al published a

fuzzy logic based novel method of quenching transients of load frequency of a single area

power system [PRA 08]. Prakash et.al investigates the impact of addition of slider gain

with fuzzy logic controller for load frequency control of interconnected thermal-thermal

and thermal-hydro interconnected power systems [PRA 09]. Ramesh et.al designed a fuzzy

logic controller which is worked for an application of HVDC link to stabilize the

frequency oscillation in a parallel AC–DC interconnected power systems [RAM 10].

Ozkop et.al presented a load frequency control for four area power systems to use fuzzy

gain scheduling of PI controller [OZK 10].

[UMR 10] proposed a new intelligent control technique is based on polar fuzzy

sets. The polar fuzzy sets were first introduced in 1990 by Hadipriono and Sun [CHA 09].

Polar fuzzy sets differ from standard fuzzy sets only in their Polar fuzzy sets are defined on

a universe of angle and hence repeat shapes every 2π radian [CHA 07]. Polar fuzzy logic

controller performs to improve the stability and dynamic performance of the power system

[ORT 95].

2.3.2.2 Neural network based techniques

In the last few years, considerable progress has been made in application of ANNs.

This is because, unlike expert systems, neural networks do not relay on a knowledge (Rule)

base, but look for and identify patterns, given appropriate design and training [AHA 02,

CHA 01, ISH 01, KAZ 02, ZEY 02]. Artificial Neural Network was not only applied for

location of faults in transmission line [CHA 99] but also in related field such as, fault

diagnosis in power systems, high impedance fault detection, fault location in sub-station, to

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mention a few. A brief comparison of various analytical techniques with ANNs for fault

location in transmission system is provided in [IBR 05]. Load frequency control using

artificial neural network is described in [DEM 01], this paper presents a suitable neural

network based solution for load frequency control problem in a deregulated environment.

The proposed control system ensures that the selected generator companies will

automatically track the load changes to keep the system frequency and tie line power

interchanges close to specified values. In the proposed control structure, it is assumed that

each control area has its own generation, distribution and transmission network, which

distribution company is responsible to track area‟s load frequency of different and

honoring tie line power exchange contracts with neighbors by securing as much

transmission and generation capacity as needed.

Generally, in the all applications, the learning algorithms cause the adjustment of

the connection weights so that the controlled system gives a desired response. Bevrani et.al

proposed a new approach based on artificial Flexible Neural Networks (FNNs) to design of

load frequency controller for a large scale power system in a deregulated environment. In

this approach, the power system is considered as a collection of separate control areas

under the bilateral Load Frequency Control (LFC) scheme [BEV 06]. In their work, in

order to greatest response and better performance, we have proposed FNN-based load

frequency controller with dynamic neurons that have wide ranges of variation. Sabahi et.al

were developed a modified dynamic neural network (MDNN) controller in two-area power

system for generating electricity with good quality [SAB 07]. Also, in this paper to achieve

the sensitivity of power systems model, neural network emulator used for identification the

power system. In PI controller, the nonlinearities of the system are not accounted for and

they are incapable of gaining good dynamical performance for a wide range of operating

conditions in a multi-area power system [AHA 06]. A strategy for solving this problem

because of the distributed nature of a multi-area power system is presented by using a

multi-agent reinforcement learning (MARL) approach [DAN 10]. It consists of two agents

in each power area; the estimator agent provides the area control error (ACE) signal based

on the frequency bias (b) estimation and the controller agent uses reinforcement learning to

control the power system in which genetic algorithm optimization is used to tune its

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parameters. This method does not depend on any knowledge of the system and it admits

considerable flexibility in defining the control objective. Also, by finding the ACE signal

based on β estimation the LFC performance is improved and by using the MARL parallel,

computation is realized, leading to a high degree of scalability. Sundaram et.al presented

an Artificial Neural Network (ANN) which is applied to self tune the parameters of PID

controller [SUN 11]. Multi area system had been considered for simulation of the

proposed self tuning ANN based PID controller.

After the evolution of soft computing tools, many researchers are tried to find better

output. So they tried newer techniques that were based on Genetic Algorithms and Particle

Swarm. These are also called optimization techniques.

2.3.2.3 Genetic Algorithm based techniques

A newly intelligent control technique is the genetic algorithms based load frequency

controller. Genetic algorithms (GA) are global search techniques, based on the operations observed

in natural selection and genetics [ABD 95, GHO 04, JUA 02]. They operate on a population of

current approximations [AL 00, MAT 02]. The individuals initially drawn at random, from which

improvement is sought. Individuals are encoded as strings (chromosomes) constructed over some

particular alphabet, e.g., the binary alphabet {0, 1}, so that chromosomes values are uniquely

mapped onto the decision variable domain. Once the decision variable domain representation of the

current population is calculated, individual performance is assumed according to the objective

function which characterizes the problem to be solved. It is also possible to use the variable

parameters directly to represent the chromosomes in the GA solution.

At the reproduction stage, a fitness value is derived from the raw individual

performance measure given by the objective function, and used to bias the selection

process. Highly fit individuals will have increasing opportunities to pass on genetically

important material to successive generations. In this way, the genetic algorithms search

from many points in the search space at once and yet continually narrow the focus of the

search to the areas of the observed best performance. The selection individuals are then

modified through the application of genetic operators, in order to obtain the next

generation. Genetic operators manipulate the characters (genes) that constitute the

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chromosomes directly, following the assumption that certain genes code, on average, for

fitter individuals than other genes. Genetic operators can be divided into three main

categories reproduction, cross over and mutation.

1. Reproduction: Selects the fittest individuals in the current population to be used in

generating the next population.

2. Cross over: Causes pairs, or larger groups of individuals to exchange genetic

information with one another.

3. Mutation: Causes individual genetic representations to be changed according to some

probabilistic rule.

Genetic algorithms are more likely to converge to global optimal than conventional

optimization techniques, since they search from a population of points, and are based on

probabilistic transition rules. Conventional optimization techniques are ordinarily based on

deterministic hill – climbing methods, which, by definition, will only find local optima.

Genetic algorithms can also tolerate discontinuities and noisy function evaluations.

In the study of [PRA 08], the optimal values of the parameters ƒ1 (or) ƒ2 and Δ 1

(or) Δ 2 which minimize an array of different performance indices are easily and accurately

computed using a genetic algorithm.

Al-Hamouz proposed the variable structure controller (VSC) feedback gains

selection by GA. The proposed method provides an optimal and systematic way of

feedback gains selection in the VSC compared to trial and error methods. The application

of the proposed method to the LFC problem reveals that not only the system performance

is highly improved but also the control effort is dramatically reduced as compared to

previous methods [AL 00]. Using GA and by proper choice of the performance index to be

evaluated, the designer can achieve a tradeoff between the frequency deviation and the

control effort.

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Padhy et.al select the fitness function on integral of the square of the area control

error (ISACE) and integral of the time multiplied absolute value of the area control error

(ITAACE) have been utilized for his genetic algorithm tool. By selecting one of the

criterions at a time the fitness function is minimized by using the genetic algorithm. By

minimizing the fitness function we get the optimal parameters of either the integral

controller or PID controller [PAD 08].

Nikzad et.al proposed that the calculation of the performance index for each of the

individuals in the current population, to do this the system must be simulated to obtain the

value of the performance index [NIK 10]. Prasanth et.al designed GA controller which

consists of two crisp inputs namely deviation of frequency and the other is derivative of

frequency deviation. In this work, they investigate the optimum adjustment of the classical

AGC using GA and performance indices, namely the integral of time – multiplied absolute

value of the error (ITAE) [PRA 08].

Shankar et.al introduced the FACTS based decentralized controller for load

frequency control of two area interconnected hydrothermal power system considering the

combined effect of RFB ( Redox Flow Batteries ) and TCPS ( Thyristor Control Phase

Shifter ) as a FACTS device which is incorporated with tie-line power flow of the system.

The proposed controller is design using genetic algorithm based integral controller in

which Integral Square Error (ISE) criterion is consider for the optimization of the system

error [SHA 12]. The proposed controller gives better transient responses and helps in better

stabilizing frequency response as well as improves the tie line power flow of the system.

Aditya et.al proposed that Optimum gain settings of different types of controllers

are obtained using the genetic algorithm (GA) for a two area hydro power system. Aditya

minimized an objective function for obtaining the optimum gain settings of integral (I), PI,

ID, and PID controllers using a genetic algorithm. It was found that an integral controller

gives unstable dynamic responses and a PI controller gives highly oscillatory dynamic

responses for two area hydro power systems because of the special characteristics of a

hydro system [ADI 10].

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Panda et.al proposed GA along with decomposition technique as developed has

been used to obtain the optimum megawatt frequency control of multi-area electric energy

systems. Time-domain simulations are conducted with trapezoidal integration along with

decomposition technique. The following two modifications have been proposed

• Modification in parent selection

• Modification in crossover mechanism

In this proposed algorithm, the following modifications have been proposed with an

intuition to have better trade-off between exploration of unknown solution space and

exploitation of already known knowledge of solution to find the global optimum in less

number of generations. In this work, one point crossover also called Holland crossover is

adopted with a probability Pc∈ [0.6, 0.95] with modifications in exchange of chromosomal

materials [PAN 10].

IBRAHEEM et.al is used to get the optimal AGC feedback gains with the help of

GA. A penalty function based strategy is used to satisfy transient response specifications of

system frequency and power flow deviations and consequently, area control error (ACE) is

minimized to zero. This proposed AGC scheme is tested on a two-area interconnected

power system consisting of thermal power plants with reheat turbines. The area

interconnections considered are with AC link only and AC link in parallel with DC link.

The response plots achieved with proposed scheme are compared those obtained with

optimal AGC regulators designed using Linear Quadratic Regulator (LQR) concept [IBR

09]. The investigation of the system dynamic responses under load disturbance conditions

reveal that proposed GA based AGC scheme yields appreciably better results as compared

to those obtained with LQR concept based optimal AGC scheme.

Arivoli et.al investigates the performance of the two-area identical, thermal

reheat systems interconnected with AC-DC tie-lines using Mutual Aid Criterion

(MAC) with Genetic algorithm (GA).The optimum proportional controller feedback

gain Kp is obtained by plotting the cost curve for various values of Kp against the cost

function of area [ARI 11].

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Bhongade et.al has been tuned PID controller according to GA based performance

indices. Their developed model also includes the Superconducting Magnetic Energy

Storage (SMES) units to inject or absorb the active power of an interconnected power

system. The functioning of Genetic Algorithm based PID controller has been tested on a

39-bus New England system and 75-bus Indian power system network [BHO 11]. The

results of controller have been compared with those obtained by using the Least Square

Minimization method.

Mandour et.al proposed three different robust controllers. The first is based on H∞

control design in order to obtain robustness against uncertainties. The second controller is a

reduced model to the H ∞ controller because the first one is very complex for practical

implementation. Genetic algorithm (GA) is used in the third controller to optimize

proportional integral differential (PID) controller parameters. Results show that the PID

controller has the best performance in damping oscillations, minimizing overshoot by a

great amount and reaching zero value in about 5 seconds [MAN 10].

Juang et.al proposed that to reduce the fuzzy system design effort and the number

of fuzzy rules, the fuzzy system is designed automatically by genetic algorithms. To

improve the design performance, a new genetic algorithm using elitist strategy combined

with similarity measure on relatives between individuals is proposed. This way, we can

reduce the de-sign effort and find a better fuzzy rule set [JUA 02].

A newly intelligent control technique is the design of a fuzzy system by

evolutionary algorithms has been proposed [JUA 04]. In this work, they apply the idea of

evolutionary fuzzy systems to the LFC problem. During control a fuzzy system issued to

decide adaptively the proper proportional and integral gains of a PI controller according the

area-control error and its change [ADI 03, CLE 02, COO 01].

Du et. Al developed an online algorithm for Fuzzy Logic Controller (FLC) with

Genetic Algorithm (GA).The complex AGC control system s are separated into individual

single-input single-output (SISO) system . This SISO cascade loop models consider the

fast load changes and slow plant utility response as different disturbances. The

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mathematical model for Multi-Area AGC system used in their paper is a four-area

interconnected power system model with reheat nonlinearity effect of the steam turbine

and upper and lower constraints for generation rate nonlinearity of hydro turbine [Du 06].

Abdennour proposes an adaptive optimal gain scheduling approach to the Load

Frequency Control (LFC) problem. The obtained gains by GA are used to train an

Adaptive Network–based Fuzzy Inference System (ANFIS) to provide a general mapping

between the operating conditions and the optimal control gains. Both approaches (GA and

ANFIS) are compared. This paper published that the ANFIS gives better result than GA

[ABD 02].

No doubt that the GA gives better results to previous techniques but it has some problems

as:

To find the optimal solution for complex high dimensional, multimodal problems

often requires very expensive fitness function evaluations.

Genetic algorithm does not handle well with complexity. That is, where the number

of elements which are exposed to mutation is large there is often an exponential

increase in search space size.

GA may have a tendency to converge towards local optima or even arbitrary points

rather than the global optimum of the problem.

Operating on dynamic data sets is difficult, as genomes begin to converge early on

towards solutions which may no longer be valid for later data.

GAs cannot effectively solve problems in which the only fitness measure is a single

right/wrong measure (like decision problems), as there is no way to converge on the

solution (no hill to climb).

For specific optimization problems and problem instances, other optimization

algorithms may find better solutions than genetic algorithms (given the same amount of

computation time) as swarm intelligence (e.g.: ant colony optimization, particle swarm

optimization).

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2.3.2.4 Particle Swarm optimization techniques

Many researchers gave different variants of PSO to solve the problem of LFC. To

improve the performance of particle-swarm optimization (PSO) hybrid methods [GHO 04,

KEN 95, KEN 97, QI 06] are proposed. PSO is a relatively new evolutionary computation

technique [ADH 07, ALR 06, ASA 04, KOK 08].

PSO is initialized by a population of random solutions and potential solution is

assigned a randomized velocity [CLE 99, DOR 08, GUA 07]. The potential solutions,

called particles, are then „flown‟ through the problem space [HIS 06, HO 05]. Each particle

keeps track of its coordinates in the problem space, which are associated with the best

solution or fitness achieved so far [LIP 03, MA 06, SHI 98]. The fitness value is stored and

it is called as pbest and globaly best value is called gbest. Thus at each time step, the

particle changes its velocity and moves toward its pbest and gbest; this is global version of

PSO [RAN 04, SID 07, VLA 04].

Gozde et.al and Nikzad et. al are proposed swarm optimization based optimal

proportional-plus-integral (PI) controller. The design is determined an optimization

problem and a novel cost function is derived for increasing the performance of

convergence to the solution [NIK 11]. To optimize the parameters of the cost functions and

the PI-controller, the craziness based particle swarm optimization (CRAZYPSO) algorithm

is used [GOZ 09]. Jayanthi et.al is proposed a controller for investigating with spinning

reserves such as Superconducting Magnetic Energy Storage device (SMES) and Gas

Turbine units. PI controller gains are tuned using an EAPSO technique to find best

parameter for the tuning of controller [ISM 12, JAY 11, OMA 07, TAH 08].

Gautam ei.al proposed Improved-PSO optimized self-tuning PID controller [BOR

11, DAS 11, GAU 10, GOZ 08, SHA 09, SOU 10]. Improved-PSO framework adopting

a crossover operation scheme to increase exploitation capability of PSO.

To ease the design effort and improve the performance of the controller, design of

the Fuzzy-PI (FPI) controller by hybridizing a genetic algorithm and particle-swarm

optimization, called FPI–HGAPSO, is proposed [EBE 01, GHO 03, HAN 02, SWA 08].

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FPI–HGAPSO is based on the hybrid of the genetic algorithm and particle-swarm

optimization [JUA 02, JUA 04, JUA 04, KRI 02, NAK 03, ROY 06, SHA 10, TSA 04]. In

FPI–HGAPSO, elites in the population of GAs are enhanced by particle-swarm

optimization and these enhanced elites are selected as parents for cross over and mutation

operations.

2.3.2.5 Other hybrid techniques

Some researchers used other optimization technique to control the system

frequency and the inter area tie power as near to the scheduled values as possible. GA is

less sensitive to local minimum as compared to the conventional approach. GA

manipulates the representation of potential solution, rather than the solutions itself. The

premature convergence of GA degrades its efficiency and reduces the search capability. To

overcome this problem a more recent and powerful computational intelligence technique

bacterial foraging (BF) is available in which the number of parameters that are used for

searching the total solution space is much higher compared to those in GA. Social foraging

behavior of Escherichia coli bacteria is used by Nanda et.al [NAN 09] and Ali et.al [ALI

10]. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is

currently gaining popularity in the community of researchers, for its effectiveness in

solving certain difficult real world optimization problems. The foraging strategy of

Escherichia coli bacteria present in human intestine can be explained by four processes,

namely chemotaxis, swarming, reproduction, and elimination dispersal. BFOA due to its

unique dispersal and elimination technique can find favorable regions when the population

involved is small. These unique features of the algorithms overcome the premature

convergence problem and enhance the search capability. Hence, it is suitable optimization

tool for power system controllers. In [NAN 09], Comparison of convergence

characteristics of BF, GA, and classical approach revealed that the BF algorithm is quite

faster in optimization, leading to reduction in computational burden and giving rise to

minimal computer resource utilization.

Pothiya et.al focused on a new optimization technique of a fuzzy logic based

proportional integral (FLPI) load frequency controller by the multiple Tabu search (MTS)

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algorithm [POT 06]. The Tabu search (TS) algorithm is an iterative search that starts from

some initial feasible solution and attempts to determine a better solution in the manner of a

hill-climbing algorithm. The TS algorithm has a flexible memory in which to maintain the

information about the past step of the search and uses it to create and exploit the better

solutions. The main two components of the TS algorithm are the Tabu list (TL) restrictions

and the aspiration criterion (AC). The proposed technique for designing a FLPI controller

helps us save time when compared to those from conventional trial and error design

procedures. Another benefit of this approach is that it does not require experts for the

design of the fuzzy logic controller.

Vrdoljak et.al presented a new discrete-time sliding mode controller for load-

frequency control (LFC) in control areas (CAs) of a power system. Design of the discrete-

time sliding mode controller for LFC with desired behavior is accomplished by using a

genetic algorithm. GA used for the purpose of finding optimal sliding mode algorithm

parameters [VRD 10]. Dong et.al presented a new approach which is based on active

disturbance rejection control (ADRC). Estimating and mitigating the total effect of various

uncertainties in real time [DON 10]. Ganpathy et.al proposed a new approach to the design

of decentralized controllers, using Multi-Objective Evolutionary Algorithm (MOEA), for

load frequency control of interconnected power systems with AC-DC parallel tie-lines.

Steady state Multi-Objective Evolutionary Algorithm is based on ε-dominance concept

[GAN 10].

Chatterjee published a concept of Dual mode control is applied in the PI

controller, such that the proportional mode is made active when the rate of change of the

error is sufficiently larger than a specified limit otherwise switched to the integral mode.

A digital simulation is used in conjunction with the Hooke-Jeeve‟s optimization technique

to determine the optimum parameters of the PI controller [CHA 11]. Their design method

for a robust controller was based on Quantitative Feedback Theory (QFT). In QFT one of

the main objectives is to design a simple, low order controller with minimum bandwidth.

To indicate the effectiveness of proposed technique, this method has been compared with a

PI controller optimized by Genetic Algorithm or Particle Swarm Optimization [SAK 11].

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Paramasivam et.al developed a sophisticated application of Redox Flow

Batteries (RFB) coordinated with Unified Power Flow Controller (UPFC) for the

improvement of Load Frequency Control of a multi- unit multi- area power system.

The UPFC offers an effective means to enhance improvement in the power transfer

capability of the tie-line. The main application of UPFC is to stabilize the frequency

oscillations of the inter-area mode in the interconnected power system by the

dynamic control of tie-line power flow. The Artificial Bee Colony algorithm is used to

optimize the parameters of UPFC and the cost function of the two area power

system along with the integral controller [PAR 11]. Soheilirad et.al proposed a new

evolutionary computing method based on imperialist competitive algorithm (ICA), which

was formulated by Gargari and Lucas, is used for tuning the parameters of a PID controller

[SOH 12].

Optimal gain tuning of PI controllers for various case studies for the LFC

problem is proposed and obtained using Artificial Bee Colony (ABC) algorithm by [JAY

12]. The Artificial Bee Colony algorithm which was introduced in 2005 by Karaboga,

is used as an optimization search simulates the intelligent foraging behavior of a honey

bee swarm. Compared with the usual algorithms, the major advantage of ABC

algorithm lays in that it conducts both global search and local search in each

iteration and as a result the probability of finding the optimal parameters is

significantly increased, which efficiently avoid local optimum to a large extent. Jayanthi

et.al were tested this controller with SMES units. Superconducting energy storage

systems (SMES) represent a fascinating prospective FACTS technology as they can

generate / absorb active and reactive power in rapid response to power system

requirements.

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