Active Power Loss Reduction by Particle Swarm Optimization Algorithm

2019 ◽  
Vol 7 (1) ◽  
pp. 904-906
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Active Power ◽  
Loss Reduction ◽  
Swarm Optimization ◽  
Power Loss Reduction

scholarly journals REDUCTION OF REAL POWER LOSS BY ADVANCED PARTICLE SWARM OPTIMIZATION ALGORITHM

2018 ◽  
Vol 6 (2) ◽  
pp. 166-181
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Test System ◽  
Superior Performance ◽  
Swarm Optimization ◽  
Real Power

This paper presents Advanced Particle Swarm Optimization (APSO) algorithm for solving optimal reactive power problem. In this work Biological Particle swarm Optimization algorithm utilized to solve the problem by eliminating inferior population & keeping superior population, to make full use of population resources and speed up the algorithm convergence. Projected Advanced Particle Swarm Optimization (APSO) algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the superior performance of the proposed Advanced Particle Swarm Optimization (APSO) algorithm in reducing the real power loss and static voltage stability margin (SVSM) Index has been enhanced.

scholarly journals AMENDED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REAL POWER LOSS REDUCTION AND STATIC VOLTAGE STABILITY MARGIN INDEX ENHANCEMENT

2018 ◽  
Vol 6 (2) ◽  
pp. 146-156
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Power Loss Reduction ◽  
Reactive Power Dispatch

In this paper, Amended Particle Swarm Optimization Algorithm (APSOA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) for solving the optimal reactive power dispatch Problem. PSO is one of the most widely used evolutionary algorithms in hybrid methods due to its simplicity, convergence speed, an ability of searching Global optimum. GSA has many advantages such as, adaptive learning rate, memory-less algorithm and, good and fast convergence. Proposed hybridized algorithm is aimed at reduce the probability of trapping in local optimum. In order to assess the efficiency of proposed algorithm, it has been tested on Standard IEEE 30 system and compared to other standard algorithms. The simulation results demonstrate worthy performance of the Amended Particle Swarm Optimization Algorithm (APSOA) in solving optimal reactive power dispatch problem.

scholarly journals HYBRIDIZATION OF ANT COLONY ALGORITHM AND PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REDUCTION OF REAL POWER LOSS

2018 ◽  
Vol 6 (12) ◽  
pp. 121-127
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Power Loss ◽  
Ant Colony Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Real Power

In this work Ant colony optimization algorithm (ACO) & particle swarm optimization (PSO) algorithm has been hybridized (called as APA) to solve the optimal reactive power problem. In this algorithm, initial optimization is achieved by particle swarm optimization algorithm and then the optimization process is carry out by ACO around the best solution found by PSO to finely explore the design space. In order to evaluate the proposed APA, it has been tested on IEEE 300 bus system and compared to other standard algorithms. Simulations results show that proposed APA algorithm performs well in reducing the real power loss.

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