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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