OPTIMAL RECONFIGURATION AND CAPACITOR PLACEMENT FOR POWER LOSS REDUCTION USING GREY WOLF OPTIMIZATION ALGORITHM

Optimal reconfiguration and capacitor placement are used to reduce power losses and keep the voltage within its allowable interval in power distribution systems considering voltage, current, and radial condition constraints. An effective method and a new algorithm using ‘‘Grey Wolf Optimization (GWO)Algorithm’’ is designed. The GWO mimics the hunting behavior and the social hierarchy of grey wolves. In addition to the social hierarchy of grey wolves, pack hunting is another appealing societal action of grey wolves. The main segments of GWO are encircling, hunting and attacking the prey. The algorithm is applied and tested on 16,33,69,118 and 119 bus IEEE test systems to find the optimum configuration of the network with regard to power losses. Five different cases are considered as mentioned below, and the effectiveness of the proposed technique is also demonstrated with improvements in power loss reduction, through MATLAB under steady-state conditions.

Case #1. Only network reconfiguration;

Case #2. Only capacitor placement;

Case #3. First, network reconfiguration and then capacitor placement

Case #4. First, capacitor placement and then network reconfiguration;

Case #5. Network reconfiguration and optimal capacitor placement, simultaneously;

Reference Paper-1: Optimal reconfiguration and capacitor placement for power loss reduction of a distribution system using improved binary particle swarm optimization

Author’s Name: Mostafa Sedighizadeh. Marzieh Dakhem. Mohammad Sarvi and Hadi Hosseini Kordkheili

Source: Int J Energy Environ Eng

Year:2014

Reference Paper-2: Reconfiguration and optimal capacitor placement for losses reduction

Author’s Name: Diana P. Montoya and Juan M. Ramirez

Source: IEEE

Year:2012

Reference Paper-3: 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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