OPTIMAL LOCATION AND PARAMETER SETTING OF TCSC FOR LOSS MINIMIZATION USING GRAVITATIONAL SEARCH, GREY WOLF AND FIREFLY OPTIMIZATION ALGORITHM

    Flexible Alternating Current Transmission Systems (FACTS) devices have been proposed as an effective solution for controlling power flow and regulating bus voltage in electrical power systems, resulting in an increased transfer capability, low system losses, and improve stability. However, to what extent the performance of FACTS devices can be brought out highly depends upon the location and the parameters of these devices. The design uses three optimization algorithm i.e. Gravitational Search Algorithm (GSA),Grey Wolf Algorithm(GWA) and Firefly Algorithm(FA).The main objective of this work is to determine the optimal location and the optimal parameter setting of the Thyristor Controlled Series Compensator (TCSC) in the power network to minimize the loss of the power system and compare their performances. To show the validity of the proposed techniques and for comparison purposes, simulations are carried out on IEEE-57 bus power system. The results are presented in the simulation video demo as given below.

Reference Paper-1: Optimal location of FACTS device using Meta Heuristic Search Algorithm
Author’s Name: A. S. Siddiqui, Tanmoy Deb, and Fahad Iqbal
Source: IEEE
Year:2015
Reference Paper-2: Power Flow Analysis of Simulink IEEE 57 Bus Test System Model using PSAT
Author’s Name:
R. Anand and V. Balaji
Source
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Indian Journal of Science and Technology
Year:2015
Reference Paper-3: Optimal Location and Parameter Setting of TCSC for Loss Minimization Based on Differential Evolution and Genetic Algorithm
Author’s Name:
Ghamgeen I. Rashed ,Yuanzhang Sun and, H. I. Shaheen
Source
: Elsevier

Year:2012
Reference Paper-4: An Improved Grey Wolf Optimizer Based on Differential Evolution and Elimination Mechanism
Author’s Name: Jie-Sheng Wang and Shu-Xia Li
Source: Scientific Reports
Year:2019
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Author’s Name: Sureshkumar Sudabattula and Kowsalya M
Source: Journal of Electrical Engineering
Year:2017

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SIMULATION VIDEO DEMO-GRAVITATIONAL SEARCH ALGORITHM                                                                                                                        

SIMULATION VIDEO DEMO-GREY WOLF ALGORITHM                                                                                                                        

SIMULATION VIDEO DEMO-FIREFLY ALGORITHM                                                                                                                        

SIMULATION VIDEO DEMO-3 ALGORITHM COMPARISON                                                                                                                        

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