COMPARISON OF OPTIMAL RECONFIGURATION AND CAPACITOR PLACEMENT FOR POWER LOSS REDUCTION USING DIFFERENT OPTIMIZATION ALGORITHM
Optimal reconfiguration and 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 different algorithms used to compare design performance. The ten algorithms as listed below are 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.
1. Teaching Learning Algorithm(TLBO)
2. Cuckoo Search Algorithm
3. Imperialist Competitive Algorithm
4. Biogeography Based Optimization
5. Cultural Algorithm
6. Dolphin Echolocation Algorithm
7. Modified Bacterial Foraging
8. Grey Wolf Optimization
9. Binary Gravitational Algorithm
10. Improved Binary Particle Swarm Optimization (IBPSO)
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: Reconfiguration of Distribution Systems to Improve Reliability and Reduce Power Losses using Imperialist Competitive
Algorithm
Author’s Name: M. Sedighizadeh(C.A.), M. Esmaili and M. M. Mahmoodi
Source: Iranian Journal of Electrical & Electronic Engineering
Year:2017
Reference Paper-4: A new optimization method: Dolphin echolocation
Author’s Name: A. Kaveh and, N. Farhoudi
Source: Elsevier
Year:2013
Reference Paper-5: 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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SIMULATION VIDEO DEMO-IEEE 16 BUS SYSTEM
SIMULATION VIDEO DEMO-IEEE 33 BUS SYSTEM
SIMULATION VIDEO DEMO-IEEE 69 BUS SYSTEM
SIMULATION VIDEO DEMO-IEEE 118 BUS SYSTEM
SIMULATION VIDEO DEMO-IEEE 119 BUS SYSTEM
