A Comprehensive review on Optimization Algorithms for Best Location of FACTS Controller

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
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A Comprehensive review on Optimization Algorithms for Best Location
of FACTS Controller
P S Vaidya1, V K Chandrakar2
1Research Scholar, Dept. of Electrical Engineering, G H Raisoni College of Engineering, Nagpur, Maharashtra,
India.
2Professor, Dept. of Electrical Engineering, G H Raisoni College of Engineering, Nagpur, Maharashtra, India.
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Abstract - Due to soaring demand and difficulties like
excessive power transfer via transmissionlines, overloading,
massive transmission losses, unstable voltage, poor power
quality, unreliability, voltage profile issues, and a
prohibitively high cost of constructing a brand-new power
grid, optimizing the use of the existingoneis moreimportant
than ever. This paper summarizes the existing and proposed
literature on optimal placementstrategiesforcompensating
devices. In total, 59 studies are reviewed, dissected, and
analyzed for their aims, optimization techniques, and
example applications. This paper is useful for analysts
looking to expand their research and study of the power
system's application in various fields related to location of
compensating FACTS controller.
Key Words: FACTS, TCSC, TCPAR, STATCOM, SVC,
UPFC, TCPST, met heuristic optimization technique,
DE, GA, PSO, IPSO, ABC, OPF.
1. INTRODUCTION
Transmission lines are being severely affected by
continuously increasing load demand, dynamic load
pattern and due to integration of system. They are
operating either in overload or under load condition. This
contradictory distribution of loads overwhelmstheprofile
of voltage and thus causes the security of system voltage
most unsafe to the faults. Hence, it set off very much
difficult to enhance the performance of network and
maintain its security and reliability [27]. Due to economic
factor establishing a new transmission line is not feasible,
hence indeed up gradation of existing transmission line is
very much necessary rather its expansion. Rather than
conventional method, which uses technologies depending
on electro-mechanic devices having very less speed and
huge cost, power electronics devices are more suitable.
Hingorani andGyugyipresentedFACTStechnologyin1999
for improvement of power transfer capability making
transmission system more flexible and stable. By using
power electronics basedFACTSdevicestheperformance of
power network gets improved and also making it reliable
and efficient [11]. For getting maximum relief from
clogging, decrement in transmission line power loss is
necessary. Proper location of FACTScompensatingdevices
becomes important as they are not very cheap.
Researchers for finding proper location of FACTS
compensating devices propose different algorithms.Usual
methods of placement of compensating devices are
classified into analytical optimization technique, Met
heuristic optimization techniquesandhybrid metheuristic
optimizationtechniques.Examinationofprimeparameters
should be executed for showing the success of the
suggested algorithm for obtaining the proper placementof
FACTS compensator in transmission network [11]. Here a
review of recent optimization methods for properlocation
of FACTS compensators for a given bus network is carried
out.
2.ReviewofResearchonOptimizationAlgorithms
for location of FACTS Compensators
2.1 Sensitivity Analysis
For the inspection of power system and also for locating
optimal location of FACTS compensatingdevice,sensitivity
analysis methods were proposed. In thisanalysis,firstlyan
index is explained and computed. Mostly indices used are
voltage sensitivity index and power loss index. This
analysis is also known as analytical approach has the
advantage of effective calculations [11][55][56].
In [2] 2001, S.N. Singh used sensitivityapproachtoobserve
the worthiness of FACTS compensator in the power line,
as emergency conditions in power system are more and
hence unable to complete the optimal power flow for
testing the worthiness of FACTS controller. TCSC and
TCPAR are located optimally by using power loss
sensitivity index. IEEE-5 bus power system is utilized as
testing network. Results show improvement in the power
system security. In [3] 2012, Kamel et.al, suggested
sensitivity index algorithm depends on active power flow
performance, it also suggest decrement of VAR losses in
the network for proper placement of FACTS controller.
Method is also used for another objective for reducing
generation rescheduling cost. TCSC controller is taken asa
FACTS compensator here.Standard5-busnetwork istaken
for Sensitivity study. In [4] 2012, A. Samimi et.al, focuses
on the objective of proper placement and optimum rating
of compensator. Algorithm is the combination of voltage
sensitivity index and loss sensitivity index. Optimal
placement and optimum rating of TCSC and STATCOM is
done here which are used as a compensator. 14-bus

network, standard IEEE case is taken forstudy.In[5]2014,
Anwar Shahzad Siddiquim et.al suggested the proper
location of TCSC and Static Compensator to overcome
over-loading conditions with fewer losses, less voltage
deviation and least price of the device. Optimal location is
identified on the basis of total reactive power loss
reduction method. 14 buses IEEE power network was
utilized as a test case. In [6] 2015, Chetan W. Jadhao et.al,
applies the sensitivity indices method by decreasing
reactive power loss of the network forproperplacementof
SVC in 14 buses IEEE network, thus enhances power
system performance. In [7] 2017, V.Srinivasa Rao et.al,
presents two-stage algorithm for optimal placement of
STATCOM. Objective being enhancement of power system
static security. Additionally the proper parameter of
STATCOM was done with 14 buses IEEE network. N-R
power flow method with five iteration was performed for
optimizing STATCOM parameters. Also proper placement
of STATCOM by sensitivity analysis is done. In [8] 2018,
saptarshi ROY et.al, presented sensitivity analysis
approach subjected for decreasing the line losses.Analysis
is done using multiple contingencies, which is performed
on WSCC 3-Machine 9-bus network and 57-bus test
standard IEEE system. Proper locationofTCSCandTCPAR
was done. In [9] 2019, Fasda Ilhaq Robbani et.al, has
analyzed influence of STATCOM placement on the most
critical voltage profile buses by sensitivity analysis P-V
curve method. For placement of STATCOM most critical
line and bus is selected by considering its loading
parameter. Java-Bali 500KV systemisusedasa testcase.In
[54] 2013, Mithu Sarkar suggested Newton-Raphson
power flow algorithm used for allocation of UPFC. Effect of
allocation of UPFC has been observed for minimizing the
transmission power loss. 30-bus IEEE network was
utilized for validation of objective. Steady-state modeling
of UPFC is done. Results shows enhancement of voltage
profile with proper allocation of UPFC. In [66] A.Hardas
et.al has used real power flow performance index method
for obtaining the optimal location of TCPAR. Outputshows
the system has less power loss, improves real power flow
and overcome the congestion situation. 5-bus network is
taken and implemented in MATLAB and results are
compared with same system but in power worldsimulator
12.0 software.
2.2 Metheuristic optimization Algorithm
For determining the optimal placement of FACTS
controller, most commonly used technology is met
heuristic optimization method. This method is highly
efficient in consideration with multi-objective and may be
population-based optimization. [11]
2.2.1 Evolution based Algorithm
Evolution strategies were discovered in 1965 [12].
Evolution based optimizations techniques are genetic
algorithm, differential evolution etc [11].
2.2.1.1 Genetic Algorithm (GA)
Prof.John Holland firstly introduced a Simple GA in 1975
[34]. It is based on biological evolution method mentioned
in Darwin’s theory. It is a conventional method, which has
faster and better result [13]. Selection, recombination and
mutation are used as operators by GA. Recombination are
known as crossover [12].
In [13] 2018, Naseer M. Yasin et.al, aim is to decrease the
reactive power loss and maximize the power flow. Proper
location and proper rating of Static Synchronous
compensator is done on standard 5 IEEE network, 30-bus
power network and Iraqi National Grid by genetic
algorithm method. Mean power factor method is also used
for finding the weakest bus in the system. In [14] 2009,
Prashant Kumar Tiwari et.al, developed a technique to
obtain the active power sharing of generators and to get
the rating and best location of FACTS controllers, Which
will be responsible for overall system cost using genetic
algorithm and traditional N-Rmethod.IEEE-30bussystem
is utilized for simulation purpose. Compensator like Static
VAR Compensator, Thyristor Controller Series
Compensator and Unified Power Flow Controller are
utilized as a compensating power FACTS device. In [15]
2010, Prakash g.Burade et.al, uses IEEE-30 bus system for
obtaining proper location of TCSC, SVC, TCPAR & UPFC by
genetic algorithm. This algorithm alsoefficientlyoptimizes
the type, and rated value of compensator. In [16] 2003, L.J.
Cai et.al, objective is to find economic operation of
generators in the network and its dispatch, which is
carried out by genetic algorithm method to allocate the
FACTS controller with its rated values. 14 buses IEEE
network is utilized as a sample for allocation of TCSC,
UPFC, TCPST and SVC. In [17] 2012, Jigar S.Sarda et.al,
suggested Genetic Algorithm for proper location of Multi-
FACTS compensator like here TCSC, SVC and UPFC tested
on 30-bus network. Three criteria results shows, without
FACTS controller, with FACTS controller and for increased
loading on the system. In [18] 2010, A. Y. Abdelaziz et.al,
For improving the load ability of power lines and
minimizes its total lossgeneticalgorithmbyconsideringits
thermal and voltage limit is used and tested on 9 bus
network for proper placement of TCSC. In [19] 2011, A.
Bhattacharyya et.al, objectiveistoimproveperformanceof
power network and upgrading an economy of power
network. Case study is performed on 30 buses IEEE
network. GA based approach is also used for improvement
of power transfer capability for interconnected power
network. Reactive load is increased from base value upto
200%. Firstly, active and reactive power flow calculations
are done and then applied GA to find the amount of
magnitudes of FACTS devices. Results show improvement
in performance and economy of system. In [30] 2018,
Omar M. Abo Gabl et.al, objective is to get optimal location
and its optimum size for FACTS controller. Two
alternatives TCSC, STATCOM and TCSC, SVC are tested. At
different overloadingconditionsoptimizationisperformed

and is formulated for steady state condition. GA is applied
on standard 30-bus network.
2.2.1.2 Differential Evolution
It is the kind of evolutionary algorithm suggested by Price
and Storn used for optimization problem. It is simpler,
significantly faster and robust [20].
In [20] 2008, M. Basu, Objective is to decreasethefuel cost
of generator by optimizing power flow control using TCSC
and TCPS controller. Modified IEEE 30 bus network is
utilized. Differential evolution gives satisfactory results
and need minimum computational time. Findings are
compared with evolutionary programming and genetic
algorithm. In [21] 2011, Ghamgeen I. Rashed et.al, has
minimizes the active power losses in the network.
Differential Evolution (DE) is used for proper location and
the proper parameter setting of TCSC. Comparison has
been done between DE and GA. Algorithm is tested on 3-
bus power network, 5 bus power networks and 14 bus
power networks. Findings show that DE is user-friendly,
rapid optimization method compared with genetic
algorithm (GA). In [22] 2011, Ahmad Rezaee Jordehi et.al,
In this more than one type of FACTS controller
optimization has been solvedusingevolutionstrategies. 30
buses IEEE network is used for testing. TCSC, SVC, UPFC
and its combination were tested using evolution strategy
algorithm.
2.2.2 Swarm based algorithm
Algorithm based on the behavior of flying insects for
tracking and reaching their food source optimally.
2.2.2.1 Particle Swarm optimization (PSO)
In 1995 Kennedy et.al, introduces PSO. It is a new met
heuristic algorithm. As species go to their destination in
ideal way, such fact is implemented for finding correct
solution for many types of optimization problem. It is a
simple and robust technique [12].
In [23] 2013, Noopur Sahu et.al, explainshowPSOmethod
is utilized for optimal location of STATCOM for
improvement of voltage profile, loss minimization, and
total Harmonic Distortion reduction in distribution &
transmission networks. 14-bus network is used for
simulation, results show that PSO was able to give
statistical significance and a great degree of convergence.
In [24] 2008, E. Nasr Azadani et.al, inthispaperobjectiveis
to enhance voltage profile,decreasingpowernetwork total
losses and increasing network loadability.UsingSTATCOM
with its proper rating fulfills objective. Particle Swarm
technique and continuation power flow method isapplied.
This technique is demonstrated on57 busesIEEEnetwork.
The algorithm is very easy to implement and enable
flexible operation. In [25] 2013, K. Ravi et.al, proposed
improved PSO for optimizing the power system
performance. Objective is to decreasethevoltagedeviation
at busses in a power system. Static Compensator
(STATCOM) is used for fulfilling the objective with proper
sizing. To illustrate the technique, 30-bus system is used.
Results show IPSO proves very efficient.In[26]2018,Reza
Sirjani, power loss index and adaptive particle swarm
optimization technique is used. Objective here being
enhancement in voltage profile,decrementin powerlosses
in network and also optimization of cost. Placement and
sizing PV-STATCOM is done.
2.2.2.2 Ant Colony optimization (ACO)
Ant Colony optimization algorithm was first introduced in
1992 by Dorigo et al [12]. Ant finds the best and shortest
route for finding food source [32]. ACO technique can be
used for optimization.
In [31] 2009, S.Sreejith et.al, Touring Ant Colony
Optimization (TACO) algorithm solve two sub-problem
simultaneously i.e. controlling power flow problem and
secondly conventional OPF problem. TCSC is used here.
Standard 30-bus network isutilizedforvalidationpurpose.
Outcome proves that TACO is suited to deal with fuzzy,
discontinuous, non-differentiable and non-convex
problem, like optimization power flow with FACTS
controller. In [32] 2019, B Brindha Sakthi et.al, proposed
ant colony optimization for finding out the proper
locations, and the proper parameter of UPFC (Unified
Power Flow Controller) device to obtain large network
load-ability in the network with less installation price. For
validation 14 and 30 bus network is used.
2.2.2.3 Harmony search optimization algorithm:-
Harmony search optimization technique was first
developed in 2001[12]. This optimization method is
inspired by music phenomenon [34].
In [33] 2009, A. Kazemi et.al, for improvement of power
system security, HSA has applied to obtain best location of
more than one type of power controller device. SVC
compensator, TCSC compensator and UPFC compensator
has been used using 30 buses network. Comparison by
considering result is done between GA and harmony
search optimization technique. In [34] 2018, D.
Karthikaikannan et.al, proper location with setting of
controller SVC and TCSC have been done by harmony
search optimization technique. It is applied on modified
30-bus power network. It is tested on lightly loaded and
heavily loaded power network used.

2.2.2.4 Gravitational search optimization
algorithm:-
Gravitational search optimization technique was first
introduced in 2009 [12]. GSA is based on Newtonian Laws
and mass interaction [36].
In [36] Dr. E. NandaKumar et.al, optimal location of UPFC
has been done by using gravitational search optimization
technique. Location and rating of UPFC is optimized using
standard IEEE 39 bus system. In [37] 2015, Venkata
Padmavathi S et.al, Gravitational search algorithm is
utilized for proper placement of FACTS controller like
TCSC, SVC and UPFC. The execution of the proposed GSA
method was performed on 30, 57-bus network. Results
exhibit improved security indexes and hence the power
system security is enhanced.
2.2.2.5 Chemical reaction optimizationalgorithm
(CRO):-
Chemical reaction optimization (CRO) was presented by
Lam et.al, in the year 2010. It is a recent algorithm
depending on the different chemical reactions [38].
In [38] 2015, Susanta Dutt et.al, shows the application of
CRO in order to fulfill objectives of enhancementofvoltage
profile, improving voltage stability and reduction in losses
present in the power network. For obtaining these
objective proper placements of STATCOM is done. 30-bus
and 57-bus IEEE network are utilized for testing. Results
implies fulfillment of objectives with better performance.
In [39] 2018, Susanta Dutta et.al, proper reactive power
dispatch (RPD) problem with flexible AC transmission
network controllerhasbeen doneusingquasi-oppositional
chemical reaction optimization algorithm. Compensating
devices used here are SVC and TCSC. To check the
supremacy of technique, it is applied on 14 buses and 30-
bus network.
2.2.2.6 Artificial Bee Colony optimization
algorithm (ABC):-
Artificial Bee Colony introduced by Karaboga in 2005 as
optimization technique whichshowstheforaging behavior
of bee colony [40].
In [40] 2016, Mohammad Rafee Shaik et.al, for
improvement in the voltage profile of power network, an
artificial bee colony optimization technique is used.
Simulation is done with and without FACTS device,
STATCOM is used as a FACTS device here and for
demonstration IEEE 30-bus system. Sizing of STATCOM is
also taken in consideration here for overall enhancement
of performance of power system. In [42] 2018, KadirAbacı
et.al, to stabilize the voltage and voltage profile and to
decrease active power loss and utilizing minimumnumber
of compensators, the artificial bee colony method is used.
Comparison of result is done with differential evolution
technique. SVC and STATCOM is considered here and
tested over IEEE 57-bus system. Techniqueisalsotested at
over-loading and to optimize the fuel cot function. In [43]
2015, Kadir ABACI et.al, uses the technique for proper
power flow in the network using SVC as a compensator.
IEEE 11-bus network and 30-bus network is utilized.
Outcomes are checked with differential evolution
technique also with reduced Hessian method. The results
obtained from differential evolution and artificial bee
colony is better than from reduced Hessian method, but
from both artificial bee colony algorithms gives much
better results. Sensitivity and continuous power flow
method is tested for real time data as 22 bus powersystem
data of Turkey. In [44] 2013, T L V Naga Lathish et.al, ABC-
OPF is tested with and without compensator SSSC on IEEE
14-bus system. Decrement of generation fuel price and
improvement of power system performance is taken as
objective. In [45] 2019, Shaik Mohammad Rafee et.al, For
improvement of system loadability, enhancement of
voltage profile and decrement in losses an artificial bee
colony technique is utilized for simultaneously locating
more than one FACTS controller. SVC, TCSC and STATCOM
is used and tested the algorithm on 14-busnetwork and30
bus network. In [46] 2019, Bairu Vijay Kumar, for
enhancement of performance of power network ABC
algorithm is used. UPFC is used as a compensator.Buswith
a maximum power loss is taken as a best locationforUPFC.
ABC technique is used to find optimum location during
generator outages. Both single generator outage and
double generator outages are taken in consideration for
optimum location of UPFC with ABC technique which is
checked on IEEE-30 bus system
2.2.2.7 Firefly algorithmoptimizationalgorithm:-
It is developed by Yang, which uses flash signals to attract
other fireflies [47].
In [47] 2018, P. Balachennaiah et.al, Real powers loss
minimization by optimizing transformer tap values and
optimization of location of UPFC is carried-out by using
Firefly algorithm. Firefly algorithm results areverifiedand
compared with another algorithm. Also bacteria foraging
technique was used to validate result. New England39bus
network and 14-bus IEEE network are utilized for testing.
In [48] 2019, Ahmed El-Sherif et.al, Optimal setting and
optimal placement of TCSC, SVC and TCPSTarecarried-out
for reactive power compensation by firefly optimization
technique. It is checked on 30 buses and 57 IEEE bus
network. Output show this method provides excellent
solutions within a very less operational time.
2.2.2.8 Whale Optimization algorithm:-
Lewis and Mirjalil first proposed this technique in 2016
[49]. WOA is initiated by Humpback–whalespecial hunting
technique [49][50].

In [49] 2020, Muhammad Nadeem, et.al, main objective is
to obtain proper location and rating of controller TCSC,
SVC controller and UPFC controller in power network.
Decrement in operating price of network that consists of
compensator and real power losses cost. Comparative
study is based on the results with Genetic Algorithm and
PSO. 14 and 30 bus network is used to check results
obtained from method.
2.2.2.9 Cat Swarm Optimization:-
Shu-Chuan Chu , Pei-wei Tsai , and Jeng-Shyang Pan
introduced this technique in 2006. This method is
motivated PSO and ACO [58].
In [58] 2006, Shu-Chuan Chu et.al, Introduces the
algorithm and also the comparison with particle swarm
optimization technique. Performance comparison is done
by applying PSO, CSO and weighting factorPSOintosixtest
functions. In [59] 2013, Enhancement of voltage stability
under large contingency is done. Optimal location of UPFC
as well as its size is determined by CSO. 3 and 14 bus IEEE
network is taken for verification.
2.3 Hybrid met heuristicoptimizationalgorithm:-
Hybrid met heuristic optimization technique is the
combination of different optimization techniques like
evolution based technique, analytical based technique or
particle swarm based techniques. It will be more
advantageous. In [51] population based evolutionary
optimization technique is used for optimal location and
sizing of TCSC.
In [52] 2017, Prof.R.K.Verma et.al, Particle swarms and
optimization technique as a hybridoptimizationtechnique
is used for optimal location SVC. IEEE 40-bus system is
used for simulation purpose. Initial population creation is
done by PSO and after GA is used for initial population and
thus continues the optimization. In [53] 2016, Sai Ram
Inkollu et.al, for enhancing the voltage profile by proper
setting of controller UPFC and IPFC is done by a new
hybrid technique PSO adaptive GSA hybrid algorithm. 30-
bus IEEE network is utilized for validation of objectives.
Power losses and injected voltages were also analyzed. In
[57] 2019, Stita Pragnya Dash et.al, Moth flame
optimization and its hybrid form as JAYA blended MFO is
used for reducing the transmission loss of system. TCSC
and SVC is used as a compensating device. 14 and 30 bus
network is taken for validation of algorithm.
Table 1:- Outline of reviewed model
Reference
paper
number
Method FACTS Device Test Case /
IEEE network
2 Sensitivity
based analysis
TCSC and TCPAR 5-bus network
3 Sensitivity
based method
TCSC 5-bus network
4 Sensitivity
based method
TCSC and Static
Synchronous
Compensator
14-bus network
5 Sensitivity
based analysis
TCSC and Static
Synchronous
Compensator
14-bus network
6 Sensitivity
based analysis
SVC 14-bus network
7 Sensitivity
based analysis
STATCOM 14-bus network
8 Sensitivity
based analysis
TCSC and TCPAR WSCC-3-
Machine-9 bus
network and
57-bus network
9 Sensitivity
based analysis
STATCOM Java-Bali 500KV
network
13 Genetic
Algorithm
STATCOM 5 bus, 30 bus
network and
Iraqi national
grid
14 Genetic
Algorithm
SVC controller,
TCSC controller
and UPFC
controller
30 bus network
15 Genetic
Algorithm
TCSC controller,
SVC controller,
TCPAR and
controller UPFC
30 bus network
16 Genetic
Algorithm
TCSC controller,
UPFC controller,
TCPST and
controller SVC
14-bus network
17 Genetic
Algorithm
TCSC controller,
SVC controller
and controller
UPFC
30 bus network
18 Genetic
Algorithm
TCSC 9 bus network
19 Genetic
Algorithm
SVC, TCSC, UPFC 30 bus network
30 Genetic
Algorithm
TCSC, STATCOM
and SVC
30 bus network
20 Differential
Evolution
Algorithm
TCSC and TCPS 30 bus network
21 Differential
Evolution
Algorithm
TCSC 5 and 14 bus
network

22 Differential
Evolution
Algorithm
TCSC, SVC, UPFC 30 bus network
23 Particle Swarm
method
STATCOM 14 bus network
24 Particle Swarm
Algorithm
STATCOM 57-bus network
25 Particle Swarm
Algorithm
STATCOM 30 bus test
network
31 Ant Colony
Optimization
technique
TCSC 30 bus test
network
32 Ant Colony
Optimization
technique
UPFC 14 bus and 30
bus network
33 Harmony
search
optimization
technique
SVC, TCSC and
UPFC
30 bus test
network
34 Harmony
search
optimization
technique
SVC and TCSC 30 bus test
network
36 Gravitational
search
Optimization
technique
UPFC 39 bus test
network
37 Gravitational
search
Optimization
technique
TCSC, SVC and
UPFC
30 and 57 bus
network
38 Chemical
reaction
Optimization
technique
STATCOM 30 and 57 bus
network
39 Chemical
reaction
Optimization
technique
SVC and TCSC 14 and 30 bus
network
40 Artificial Bee
Colony
Optimization
STATCOM 30 bus test
network
42 Artificial Bee
Colony
Optimization
SVC and
STATCOM
57 bus network
43 Artificial Bee
Colony
Optimization
SVC 11, 30 bus
power network
and 22 bus
power network
data of Turkey
44 Artificial Bee
Colony
Optimization
SSSC 14 bus network
45 Artificial Bee
Colony
technique
SVC, TCSC and
STATCOM
14 and 30 bus
network
46 Artificial Bee
Colony
technique
UPFC 30 bus network
47 Firefly
Optimization
technique
UPFC New England 39
and 14-bus
network
48 Firefly
algorithm
Optimization
technique
TCSC, SVC and
TCPST
30 and 57 bus
network
49 Whale
Optimization
technique
TCSC controller,
SVC controller
and UPFC
controller
14 and 30 bus
network
52 Hybrid met
heuristic
optimization
techniques
(PSO + GA)
SVC 40 bus network
53 Hybrid met
heuristic
optimization
techniques
(PSO +
adaptive GSA)
UPFC and IPFC 30 bus network
57 JAYA blended
MFO hybrid
met heuristic
SVC and TCSC 14 and 30 bus
network
59 Cat
Optimization
Technique
UPFC 3 and 14 bus
network
60 Real Power
Flow
Performance
Index
TCPAR 5 bus network
3. CONCLUSIONS
This paper furnishes a reported literature work done on
various optimization methods for best location of power
controllers or FACTS controllers. The paper also confers a
brief overview on optimization methods used for
accomplishing various objectives for power system
network along with various test cases of power network.
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