26

CHAPTER 2

LITERATURE SURVEY

2.1 SURVEY ON VARIOUS SOLUTION METHODOLOGIES TO

THE OPF PROBLEM

The majority of the techniques discussed in the literature use one of

the following five methods. 1. Lambda Iteration method, also called the Equal

method 4. Linear Programming method 5. Interior Point method (Ewald &

Angled 1964; Watts 2003; Bullock 1990). In addition to the above techniques

Viz., Quadratic Programming (QP), Artificial Neural Network (ANN), Fuzzy

Logic (FL) method, Genetic Algorithm (GA) (Osman et al 2004) method,

Evolutionary Programming (EP), Ant Colony Optimization (ACO)

(Vlachogiamnnis et al 2005), PSO Nonlinear programming (Habiabollahzadeh et

al 1989), Quadratic Programming (Lipowski & Charalambous 1981),

Newton-based techniques, and Linear Programming (Palomino & Quintana

1986) are also used to solve the OPF problem. There are certain

disadvantages of using numerical methods in solving OPF problem. The most

important of these is easy obtaining for the local minimum and starting point

problems. Today, heuristic methods are used to eliminate the above

disadvantage. The biggest advantage of heuristic method is that they can

obtain global minimum or optimal solutions close to the global minimum in a

short duration of time. The GA, PSO, ACO, Differential Evolution (DE)

(Sayah & Zehar 2008), and EP are the examples of heuristic methods.

27

Lima et al (2003) used Mixed Integer Linear Programming (MILP)

to conduct design study on the optimal placement of Thyristor Controlled

Phase Shifter Transformers (TCPSTs) in large-scale power systems. It finds

the number, network location and settings of phase shifters that maximize

system load ability under the DC load flow model subject to limit on the

installation investment or total number of TCPSTs. Computation time is

significantly lower than those of other published comparable cases.

Lo et al (2004) proposed two Newton-like load flow methods, the

fixed Newton method and the modification of the right-hand-side vector

method for line outage simulation that is a part of contingency analysis. These

two methods are compared with the Newton-based full AC load flow method

and the fast decoupled load flow to show their better convergence

characteristics.

Tong et al (2005) presented the semi-smooth Newton-type

algorithms for solving OPF problems. It treated general inequality constraints

and bounded constraints separately. By introducing a diagonal matrix and the

nonlinear complementarily function, the Karush Kuhn Tucker (KKT) system

of the OPF is transformed equivalently to a system of non-smooth bounded

constrained equations. The number of variables is less compared to other

method. The method saves computing cost.

Granelli et al (2000) proposed Security - Constrained Economic

Dispatch (SCOPF) using Dual Sequential Quadratic Programming (DSQP).

A dual feasible starting point is achieved by relaxing transmission limits and

then constraint violations are enforced using the Dual Quadratic Algorithm

(DQA). It is compared with SQP method of NAG routine. It presents limited

computation time and sufficiently good accuracy. Lin et al (2003) integrated

cost analysis and voltage stability analysis using an OPF formulation for

28

competitive market, which was solved using Sequential Quadratic

Programming.

Berizzi et al. (2005) presented Security Constrained Optimal

Power Flow (SCOPF) to determine optimal setting and operation mode of

UPFC and TCPAR (Thyristor Controlled Phase Angle Regulator). The

solution of the OPF is obtained by the use of the HP (Han-Powell) algorithm.

It is an efficient method that solves nonlinear problems with nonlinear

constraints through the solution of successive quadratic problems with linear

constraints. It is applied to CIGRE 63-bus system and Italian EHV network.

Torres et al (2002); Chowdhary (2002) suggested the methods to

calculate the price of reactive power support service in a multi-area power

system. The methods are Cost-Benefit Analysis (CBA) and nonlinear convex

network flow programming. By using multiple cost-benefit indices, the

reactive power support benefits with respect to power delivery increases of tie

lines are computed. Sharma (2006) had proposed a method to determine

optimal number and location of TCSC using Mixed Integer Nonlinear

Programming (MINLP) approach in the deregulated electricity markets. The

system load ability has been determined in a hybrid market model utilizing

the secure transaction matrix.

Lin et al (2000) presented the use of Predictor-Corrector Interior-

Point Nonlinear Programming (PCIPNLP) algorithm to solve social welfare

maximization problem incorporating FACTS devices in a deregulated market

structure. FACTS can ease the difficulties caused by transmission congestion.

The advantage of this method is the inequality constraint handling

capabilities. Active set identification for handling various upper and lower

limits is not required. A strictly feasible starting point is not mandatory.

Xiaoying et al (2002) presented an Interior Point Branch and Cut Method

(IPBCM) to solve Decoupled OPF problem. The Modern Interior Point

29

Algorithm (MIPA) is used to solve Active Power Sub Optimal Problem

(APSOP) and use IPBCM to iteratively solve linearization of Reactive Power

Suboptimal Problem (RPSOP).

Wei et al (2006) presented the solution of the Optimal Reactive

Power Flow (ORPF) problem by the Predictor Corrector Primal Dual Interior

Point Method (PCPDIPM). A new optimal reactive power flow model in

rectangular form is proposed. The Hessian matrices in this model are

constants and needed to be evaluated only once in the entire optimal process.

Total calculation time needed for the proposed method is always shorter than

that for the conventional model for the seven test cases.

Santoso et al (1990) presented a two-stage Artificial Neural

Network (ANN) to control in real time the multi tap capacitors installed on a

distribution system for a non-conforming load profile such that the system

losses are minimized.

Ramesh et al (1997) presented a Fuzzy Logic approach for the

contingency constrained OPF problem formulated in a decomposed form that

allows for post-contingency corrective rescheduling in which linear

membership function is used. However, choice of a suitable metric for

measuring correction time is unclear. Padhy (2004) presented an efficient

hybrid model for congestion management analysis for both real and reactive

power transaction under deregulated Fuzzy environment of power system.

Security constraint (Somasundaram et al 2004), the harmonic

constraint (Yi 1997), voltage stability constraint (Kim et al 2001), and the

transient stability constraint (Mo et al 2007; Hoballah & Erlich 2007) can be

given as examples of restrictions on the issue. However, despite these

limitations, PSO is a robust stochastic optimization technique based on the

30

movement and intelligence of swarms. It applies the concept of social

interaction to problem solving.

Attaviriyanupap et al (2005) presented a new bidding strategy for a

day-ahead energy and reserve markets based on an EP. The optimal bidding

parameters for both markets are determined by solving an optimization

problem that takes unit commitment constraints such as generating limits and

unit minimum up/down time constraints into account. Jayabarathi et al (2005)

proposed the application of classical EP, Fast EP and Improved FEP methods

to solve all kinds of economic dispatch problems such as ED of generators

with Prohibited Operating Zones (POZ), ED of generators with Piecewise

Quadratic Cost Function (PQCF), Combined Economic Environmental

Dispatch (CEED) and Multi-Area Economic Dispatch (MAED).

2.2 SURVEY ON FACTS CONTROLLER DEVICES

INCORPORATION TO OPF PROBLEM WITH VARIOUS

ALGORITHMS

Chung & Li (2000) presented a Hybrid Genetic Algorithm (HGA)

method to solve OPF incorporating FACTS controller devices. GA is

integrated with conventional OPF to select the best control parameters to

minimize the total generation fuel cost and keep the power flows within the

security limits. TCPS and TCSC are modelled. The proposed method was

applied on modified IEEE 14 bus system and it converged in a few iterations.

Cai et al (2004) proposed optimal choice and allocation of FACTS controller

devices in multi-machine power systems using genetic algorithm. The

objective is to achieve the power system economic generation allocation and

dispatch in deregulated electricity market. The locations of the FACTS

controller devices, their types and ratings are optimized simultaneously.

UPFC, TCSC, TCPST and SVC are modelled and their investment costs are

also considered.

31

OPF problem is the perfect amalgamation of the contradictory

doctrines of greatest economy, safer operation and increased security

(Bouktir & Slimani 2005). Usually, the OPF is considered as the

minimization of an objective function describing the generation cost and/or

the transmission loss (Bhaskar et al 2010; Chayakulkheeree & Ongsakul

2007). The OPF problem solution tries to optimize a selected objective

function such as fuel cost through optimal regulation of the power system

control variables, while satisfying several equality and inequality constraints

(Mathur & Varma 2002).

Sidhu et al (2005) evaluated the performance of distance relays on

transmission lines with FACTS devices applied for midpoint voltage control.

Effect of two types of shunt FACTS controller devices, SVC and STATCOM

are compared at three stages.

Vijayakumar & Kumidinidevi (2007) proposed a novel method for

optimal location of FACTS controllers in a multi machine power system

using GA where using the proposed method, the location of FACTS

controller, their type and rated values are optimized simultaneously. Among

the various FACTS controllers, Thyristor Controlled Series Compensator

(TCSC) and UPFC are considered. The proposed algorithm is an effective

method for finding the optimal choice and location of FACTS controller and

also in minimizing the overall system cost, which comprises of generation

cost and investment cost of FACTS controller using GA and conventional

Newton -

for Genetic Algorithm. In order to verify the effectiveness of the proposed

method, IEEE 9 bus system is used. The total population size selected is 150,

where the mutation probability as 0.01 and cross over probability as 1.0. The

proposed algorithm is an effective and practical method for the optimal

allocation of FACTS controllers.

32

Wang et al (2007) evaluated the computational challenges with

three separate techniques, namely, Trust-Region based Augmented

Lagrangian Method (TRALM), Step-Controlled Primal-dual Interior point

Method (SCIPM), and Constrained Cost Variable (CCV) OPF formulation,

are proposed here for reliable and efficient computation of large-scale market-

based OPFs. Numerical studies showed that these techniques were reliable

and better than some existing ones in solving large-scale market-based on-

smooth OPFs. DPOB-PDIPM and CCV - PDIPM are particularly good for

real-time applications due to their efficiencies, while SCIPM and TRALM

can be applied to solve problems that cannot be reformulated through DPOP

or CCV. However, in the case where security enhancement is considered, the

complexity of the problem requires the optimization process to be aided by

GA (Yorino et al 2003; El-Araby et al (2002). The main conclusions about the

MINLP formulation indicate that the size and the non-convexity of the

problem, which depend on the system parameters, are critical issues that may

cause a convergence problem in the algorithm. Authors have proposed the

investigation of modern heuristics methods to address the non-convex cases

(Yorino et al 2003).

Bai & Wei (2009) proposed a promising Semi-Definite

Programming (SDP) method for the Security Constrained Unit Commitment

(SCUC) problems because of its excellent convergence and the ability of

handling the non-convex integer variables. Different test cases from 6 to 118

buses over a 24 hour horizon are presented. Extensive numerical simulations

have shown that the proposed method is capable of obtaining the Optimal

Unit Commitment (OUC) schedules without any network and bus voltage

violations, and minimising the operation cost as well.

Pudjianto et al (2002) used LP and NLP based reactive OPF for

allocating (auctioning) reactive power among competing generators in a

33

deregulated environment. It was concluded that the overall cost associated

with the system reactive requirement calculated by LP method was reasonably

Suth & Kamaraj (2008) present the application of PSO algorithm to

find the optimal location of multi type FACTS devices in a power system in

order to eliminate or alleviate the line over loads. The optimizations are

performed on the parameters, namely the location of the devices, their types,

their settings and installation cost of FACTS controller devices for single and

multiple contingencies. The effectiveness of the proposed method is tested for

IEEE 6 bus and IEEE 30 bus test systems. Glanzmann & Andersson (2005)

derived a supervisory controller based on OPF with multiple objectives in

order to avoid congestion, provide secure transmission and minimize active

power losses. Finally, simulations showing the improvements of the derived

control are presented as follows. Congestions are resolved, voltage profiles

became more balanced and active power losses are reduced.

Tuaimah & Meteb (2012) objective is to find steady state operation

point which minimizes generation cost, loss and load ability etc. while

maintaining an acceptable system performance in terms of limits on

generators real and reactive powers, line flow limits with the help of an OPF

algorithm. A PSO is proposed to solve the OPF problem. The PSO is used to

minimize the generating cost as an objective function through the inequality

constraints as well as the conventional load flow is used to perform the

equality constraints. A computer program, written in MATLAB environment,

is developed to represent the proposed method. The adopted program is

applied for the first time to Iraqi 24 bus Super High Voltage (SHV) network

(400 kV).

Hazra & Sinha (2010), this paper present a multi-objective OPF

technique using PSO. Two conflicting objectives, generation cost, and

34

environmental pollution are minimized simultaneously. A multi objective

PSO method is used to solve this highly nonlinear and non-convex

optimization problem. A diversity preserving technique is incorporated to

generate evenly distributed Pareto optimal solutions. Simulation results show

that PSO with dynamic velocity control ensure good convergence and explore

better solutions than PSO with Constriction Factor Approach (CFA).

Simulation results also show that proposed method is computationally

efficient and generate a set of uniformly distributed set of Pareto-optimal

solutions in single run. The algorithm is tested on IEEE 30 and 118 bus

systems and its effectiveness is proven through the results.

Kottala & Kanchapogu (2012) incorporate the FACTS device

SSSC in OPF solutions to enhance the performance of the power systems.

The PSO is used for solving the OPF problem for steady-state studies. The

effectiveness of the proposed approach was tested on IEEE 14 bus and IEEE

30 bus systems with FACTS controller device (SSSC). This model was able

to solve power networks of any size and converges with minimum number of

iterations and independent of initial conditions. Results show that the

proposed PSO algorithm gives better solution to enhance the system

performance with and without SSSC device.

Ayani & Kilic (2013), in this paper found the transient stability

constraint should be taken into consideration for the solution of the

optimization problems in power systems. This paper presents a solution for

the Transient Stability - Constrained Optimal Power Flow (TSCOPF) by a

novel approach based on the artificial bee colony algorithm. The formulae of

TSCOPF are derived through the addition of rotor angle inequality constraints

into OPF relationships. In this nonlinear optimization problem, the objective

function is taken into consideration as the fuel cost of the system. The

proposed approach is tested on a WSCC 3 generator, 9 bus systems and an

35

IEEE 30 bus system, and the test results obtained are compared to prove the

efficiency and effectiveness of the approach with other approaches in the

literature. In this paper, the Ant Bee Colony (ABC) algorithm is successfully

applied for the solution to the TSCOPF problem for the first time. Here, 2 test

systems and 3 fault cases are taken into consideration. For these cases, the

TSCOPF problem is solved by the ABC algorithm. The solutions obtained by

the ABC algorithm are compared with those obtained by other heuristic

methods in the literature.

Kumar & Raju (2012) present the solution of OPF using PSO. OPF

is an optimizing tool for operation and planning of modern power systems.

The main goal of this paper is to verify the viability of using PSO problem

composed by different objective functions. This OPF problem involves the

optimization of various types of objective functions while satisfying a set of

operational and physical constraints while keeping the power outputs of

generators, bus voltages, shunt capacitors/reactors and transformers tap

settings in their limits. The proposed PSO method is demonstrated and

compared with EP approach on the standard IEEE 14 bus system. The

generation cost and real power losses are reduced through adjustment of

generator outputs, generator voltages, tap changing transformers, and shunt

compensation. The results show that the proposed PSO method is capable of

obtaining higher quality solutions efficiently in OPF problem.

Niknam et al (2011) proposed a Modified Honey Bee Mating

Optimization (MHBMO) to solve the Dynamic Optimal Power Flow (DOPF)

problem of power system considering the valve-point effects. DOPF is a

complicated non-linear problem that occupies an important role in the

economic operation of power system. It has non-smooth and non-convex

characteristics when generation unit valve-point effects are taken into

account. Non-linear characteristics of the power generators and practical

36

constraints, such as ramp rate constraint, transmission constraints and non-

linear cost functions, are all considered for the realistic operation and they

cause much complication of the proposed problem. Recently, evolutionary

algorithms are devoted to solve compliment problems like the OPF problem.

HBMO is one of the evolutionary algorithms considered as a typical swarm-

based approach to optimization, in which the search algorithm is inspired by

the process of real honey-bee mating. Besides the privileges of HBMO, it has

some limitations such as probability of trapped in local optima and converge

to global optima in long time.

Aravindhan et al (2012) presented an efficient and reliable

evolutionary-based approach to solve the OPF problem the different

objectives are fuel cost minimization, voltage profile improvement, and

enhancement. The proposed approach employs Hybrid Particle Swarm

Optimization (HPSO) algorithm for optimal settings of OPF problem control

variables. The problem is decompressed into the optimal setting of TCSC

parameter sub problem that is searched by the hybrid PSO approach and the

OPF with fixed FACTS parameters sub problem that is solved by the

Modified Interior Point Programming method. The proposed approach has

been examined and tested on the standard IEEE 30 bus test system.

Chandrasekaran et al (2009) has proposed a PSO for the solution of

OPF with the use of controllable FACTS controller devices. Two types of

FACTS devices, such as TCSC and Thyristor Controlled Phase Shifters

(TCPS) have been used. The given power flow control constraints as well as

the normal conventional constraints have been included in the OPF problem.

A sensitivity analysis has been performed for finding the optimal location of

FACTS controller devices. The proposed method has provided an improved

economic solution using the controllable FACTS controller devices.

37

s proposed an OPF based on Improved PSO

(OPF-IPSO) with generator capability curve constraint, which is used by NN-

OPF as a reference in order to obtain the pattern of generator scheduling. The

NN-OPF has been designed based on three stages. In the initial stage, the

OPF-IPSO has been designed by generator capability curve constraint. In the

second stage, the load has been clustered to a certain range and its index has

been calculated.

Saha (2010) has presented an approach which combines proper

linear models for FACTS devices with a potent optimization model including

security constraints which are essential when optimizing existing power

systems within the new framework. The SSSC has provided a fast series

compensation and flexible power system control by regulating the basic

power system parameters on which system performance depends. Hence, the

active and reactive power flows have been controlled and achieved an

efficient power transfer capability of the existing power network.

Devaraj & Yegnanarayana (2005) have proposed a technique based

on Enhanced Genetic Algorithm (EGA) to solve the OPF with FACTS

devices in order to remove the line overloads in the system and the single line

outages. The optimizations have been performed using two parameters such

as the location of the devices and their values. TCSC and TCPS Transformers

are the two different types of FACTS controllers, which have been used for

steady state studies.

Krishnasamy (2011) has proposed a GA based OPF in order to

identify the optimal location and rating of the UPFC in power systems and

also to simultaneously determine the active power generation for diverse

loading condition. The performance of power system has been evaluated by

using the overall system cost function which comprises generation cost of

power plants and the investment costs of UPFC.

38

Sinsuphun et al (2011) have proposed an OPF based on swarm

intelligences, where the power transmission loss function is used as problem

objective. Generally, most of the OPF problems contain the total production

cost of the whole power system, but in some cases, some different objective

may be selected. Four decision variables have been contributed to reduce the

overall power transmission losses. They are i) power produced from

generating plants, ii) particular voltage magnitude at control substations, iii)

tap position of tap changing transformers and iv) reactive power injection

from reactive power compensators. Swarm intelligences are superior and

greatly recognized as potential intelligent search techniques for solving such a

problem.

Vijayakumar (2011) has proposed an algorithm using OPF for

congestion management and it has been solved using GA to determine the

global optimal generation schedule for increasing the social welfare. While

performing a transaction, it has been observed that the line loading was

increased, i.e., congested. To reduce the congestion, several types of FACTS

controller devices have been located optimally by considering thermal limits

of the lines. Analysis has been performed by using two types of FACTS

controller devices. Using three UPFCs, line loading has been greatly reduced

than the three TCSCs. The proposed scheme was tested on IEEE 57 bus

system and it can be easily extended to any practical network.

Finally, evolutionary computation techniques have been extensively

applied including: (i) GA (Yorino et al 2003; El-Araby et al 2002; Gerbex

et al 2001; Ippolito & Siano 2004; Radu & Besanger 2006, 2005; Wang &

Shresth 2001; Cai et al 2004; Alabduljabbarm & Milanovic 2006;

Kaewniyompanit et al 2004; Najaf et al 2006; Kazemi et al 2006;

Arabkhaburi et al 2006; Shaheen et al 2007; Gerbex et al 2003 ), (ii) PSO

(Shaheen et al 2007; Saravanan et al 2005; Mori & Maeda 2006), (iii) SA

39

(Gerbex et al 2003; Ongsaku & Jirapong 2003), (iv) TS (Ongsaku & Jirapong

2003; Mori & Goto 2000), and (v) EP (Hao et al 2004; Ongsaku & Jirapong

2005). Other methods present in the literature include evolution strategies

(ES) (Luna & Maldonado 2006), Artificial Intelligence (AI) (Karami et al

2006), frequency response (Ramirez & Coronado 2001), sequence component

(Mahdad et al 2006), sequential number (Alabduljabbarm & Milanovic 2006),

stability index (Ishimaru et al 2002), and voltage phasor (Sharma et al 2003).

The main intention of the OPF is to regulate the optimal operating

setting of a power system and the same setting of control variables for

economic operations and meantime satisfying various equality and inequality

constraints. In present times, OPF is very crucial for operation and control of

power system due to increasing power generation cost, energy scarcity and

continuous increase in electric energy demand. The problem of OPF is

largely involved in regulating an optimal operating point of a power system,

which decreases a certain objective function such as loss of power or

generation cost connected to network and physical constraints. Among

various operational objectives, extensively considered objective is an OPF

problem may be developed to decrease the cost of fuel. The economic and

environmental constraints have the pressure to force the utilities of power to

cover the demand in future by absolutely utilizing the existing resources of

transmission facilities without newlines building. Shunt FACTS controller

devices are spent for transmission voltage control, flow of power, reactive

losses reduction and power system damping oscillations for high power

transfer levels. In universe, for various applications various FACTS controller

devices have been introduced. There are many different and advanced types

of devices are in practice. FACTS technology introduces advance

opportunities for power controlling and usable capacity enhancement of

present and also new and upgraded lines.

40

In the study of Ramesh & Reddy

has been tested in IEEE 14 as well as IEEE 30 bus systems. Along with

Genetic Algorithm, voltage stability index approach was also implemented to

identify the OPF controller location. The implementation of UPFC size

enhances the voltage values and thereby reduced power losses as compared

UPFC FACTS controller various aspects of NR power flow analysis for IEEE

30 bus system is considered. To increase the performance of system, the flow

of power and the voltage line in numerous transmission lines along with and

without the UPFC placement has been acquired.

Mori & Goto (2000) presented a Parallel Tabu Search (PTS) based

method for determining optimal allocation of FACTS controller devices in

competitive power systems. Available Transfer Capability (ATC) was

maximized with the FACTS devices. UPFC was modelled and concept of

incremental load rate was used. The proposed method was compared with

Simulated Annealing, Genetic Algorithm and Tabu Search methods. It is 1.95

and 2.68 times faster than TS and GA respectively. It contributed higher

quality solutions and it is not disturbed by initial conditions.

The line real power flow performance index can be increased by

injecting active and reactive in voltage components. For optimal placement of

UPFC, Burade & Helonde (2012) employed the line loading security

Performance Index. By using IEEE 30 bus system, this methodology has been

voltage line components decreases the flow of power in the real power flow

performance index. Anyhow, Increase in system loading does not change the

optimal placement. Singh et al (2010) conducted the same study to increase

41

sensitive indices basis. For validating the )

accuracy, formulation of OPF is employed and it was tested in both NREB

was validated by Anitha & Arul (2013) on the IEEE 30 bus system. As per

their work, flow in heavy loaded lines can be reduced by FACTS and by that

declines the loss of power system and improves the stability of the system.

The effect of SSSC and UPFC has been explained by using MATLAB and

NP algorithm based program.

influence in the voltage profile progression of power system networks. The

stability and through that increased loading. The L-index has been calculated

by incorporating the mathematical model of the UPFC in the load flow

algorithm for the different values of the control parameter r and . It was

observed that during the analysis that the placement of the UPFC

compensating device at the weakest bus is not necessarily most beneficial to

the stability of the system.

OPF model including FACTS controller device such as TCSC and

it has been using the PSO algorithm for the system performance

improvement. By using the MATLAB simulation program, the power

iterations and exclusive of initial conditions. In order to obtain solution for

OPF problem, FACTS controller device such as TSCS device has been

incorporated to reduce the generator fuel cost with the help of hybrid PSO

technique.

Similar study was conducted by Karthik & Arul (2013) to control

power flow using FACTS controller devices. They utilized both SVC and

42

TCSC for an optimization of problem and NR method has been employed to

resolve the problem of OPF. In IEEE 30 bus system the suggested method has

been tested. This study finally concluded that the incorporation of both TCSC

and SVC has drastically controlled the overloading of power system,

regulating the flow of power through a transmission line, and without

generating rescheduling to reduce the power flow.

Rajasekhar & Padma (2013) conducted the study in the same year

deducing generation cost and loss of power using PSO. In the IEEE 30 bus

system it has been tested. Solution for total cost optimization with and

without installation of TCSC by considering the generator real and reactive

power output limits and reactive power outputs, transformer tapings and bus

voltages have been obtained. The final result of the study indicated OPF

problem can be solved effectively by PSO technique through the FACTS

controller TCSC. After that to reduce the cost of generation and loss of

power; improve voltage angles, voltage profiles and voltage stability L-index.

One more study was conducted by Kannemadugu & Lakshmidevi

comparison has been done with genetic algorithm. The final outcome of the

study concluded that with PSO the performance studies in terms of voltage

profile and power loss are more accurate compared with GA. Accurate result

can be retrieved by using PSO. Jebaraj et al (2012) have done a study on

tion of

power loss. Similar to previous study, Jebaraj and his colleagues also

employed PSO for identifying the optimal location of TCSC and SVC.

43

loading capability was studied by Sahasrabudhe & Pandey (2013). This study

further studied the importance of TCSC in the VAR management which is

very essential for minimizing the power loss in the transmission line. VAR

including the controller of FACTS such as TCSC, SVC and STATCOM. The

appropriate placement of FACTS controller device in IEEE 30 bus system

revealed considerable improvement in the transmission line. Specifically, the

placement of TSCS has shown drastic improvement in the value loading

parameters. In accordance with this study, recently Jamhoria (2014)

conducted a study on the efficacy of TCSC for voltage stability improvement.

One of the important members of FACTS family is TCSC. By installing

TCSC in a suitable place it supports to minimize the flow of overloading

power in heavy lines so that it enhances the network stability. The stability of

voltage is analyzed by line stability index, which is capable of determining

tested in IEEE 30 bus

system and to identify the optimal location line stability index has been used.

The finding of this study indicated that the optimal placement of TCSC has

fiercely reduced the power loss and enhance voltage stability.

Similarly, Nabavi et al (2010) conducted the study on the social

welfare maximization by using TCSC for congestion management. To find

out the suitable location and size of TCSC, a GA has been proposed for

congestion management with the goal of social welfare enhancement while

TCSC cost was incorporated. The simulation results were successfully tested

on the IEEE 30 bus system. The final outcome of the study indicated that

incorporation of TCSC using GA algorithm increased the social welfare from

7854.920 US$/h to 8000.50 US$/h. Anyhow, this method has been tested only

44

on IEEE 30 bus system despite it can be prolonged to practical network of

any.

Next, TCVR, TCSC and SVC were made use and tested with bus

systems such as IEEE 14 and 30. at the

method that was proposed had the ability to lower the loss of power and also

UPFC, SSSC and STATCOM were incorporated in the study. The solution

proposed for the OPF issue is through using the Hybrid PSO technique that

lowers the cost of the fuel of the generator related to TCSC device. The

results of the computation showed that the proposed HPSO improved the

searching ability and enhances the search quality.

Apart from the above mentioned results, same type of study was

conducted by Bhaskar and his colleagues in 2009 to determine the

performance efficiency of TCSC and SVC on improvement in the voltage

profile. These dual controllers enhanced the profile of voltage as the duo

made use of the load end to maintain the level of voltage within the advised

limit. SVC has a parallel connection with a load while TCVR offers series

injection. The 10% difference of coordinated control of voltage TCVR injects

the in phase voltage with the bus voltage that is already prevailing. If the

difference is more than 10%, then the TCVR would function in a better way

and the reactive power of SVC that is injected would help to maintain the

levels of voltage. This change is tested by using IEEE 14 and 30 bus systems

and the results of the study were found to be highly satisfactory.

Chang & Chang (2013) performed a latest study which was carried

on to lower the issues of OPF by using the FACTS controller devices TCSC

and SVC. Instead of utilizing PSO, the study made use of the vector technique

and Performance Index of real power flow and MDCP to find the exact

45

location. The exact locations of each scheme of SVC and TCSC installations,

the MDCP lowers considerably in a continuous way with non-linear

optimization while improving the efficiency of the computation. Lastly, to

keep up with the concerns those are technical and economical in nature, larger

utilization index value is recommended by IEEE 14 bus system. These power

systems can be used practically for the method proposed in the study.

The performance of was revealed by other study

which was held by Jebaseelan & Prabu (2012) who made use of STATCOM

and also without using it. This is a FACTS kind of controller that could

provide the power of reactive at a lower rate of bus voltage, while absorbing

power while it enjoys huge storage of energy. As the load of the reactive

power changes every now and then a compensator with a quick response is in

demand. This is why STATCOM was used with the help of a simulation

program known as the MATLAB. The study held by Sarda et al (2012)

offered an innovative method to locate the optimum devices of FACTS within

a machine with multi power system with GA. By using this proposed method,

the value of the rates of FACTS controllers, the method, type and the location

were increased simultaneously. The FACTS controllers such as the UPFC,

TCSC and SVC were greatly considered for the study. The proposed

algorithm for the study was very effective as it located the FACTS controller

devices while improving the ability of the load for the line while cutting down

GS+A losses and the power flow method was conventional.

Similar study was held by Abido & Hulail (2002) who were said to

have proposed the GA/PSO algorithm in order to detect and solve the right

locations of FACTS controller devices. This study was held to determine the

ability of GA to overcome the issue of optimal location thereby merging the

46

The significance of FACTS controller devices were also analysed

by the Jumaat and his group in 2012 who offered a presentation on the right

location and size of many FACTS controller devices which were based on

PSO. This also considers the installation cost and enhances the voltage

profile. The FACTS controller devices like SVC and TCSC were employed

thereby to maintain a perfect power security system. The installation of

FACTS controller devices was also conducted to evaluate the system and its

impact owing to the reduction of loss and improvement in the voltage profile.

The application validation of IEEE was for 30 bus system and the PSO and

EP was used for 26 and 30 buses respectively for the purpose of

minimization.

The OPF model that featured FACTS controller devices like UPFC,

STATCOM, TCVR and SVC were presented using PSO algorithm enhancing

the performance of power system. All of the models specified above have the

ability to lower the iteration numbers and are not dependant initially.

According to the study by Kumar in the year 2012, all the models were tested

using IEEE 30 bus system as a standard one.

The results obtained for steady state analysis (Yorino et al 2004;

El-Araby et al 2002; Gerbex et al 2001; Ippolito & Siano 2004; Besanger &

Radu 2006; Wang & Shrestha 2001; Najafi et al 2006; Kazemi et al 2006;

Arabkhaburi et al 2006; Shaheen et al 2006; Gerbex et al 2003; Saravanan

et al 2005; Mari & Maeda 2006; Hao et al 2004), are satisfactory in finding

global optimum in reasonable computational time; however there is no clear

indication that one algorithm outperforms all others. Several evolutionary

computation techniques and hybrid versions can be used to address power

system problems but the results are highly dependent on the nature of the

problem and the implementation of the algorithm.