Open AccessAMELIORATED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM
Author(s) -
K. Lenin
Publication year - 2018
Publication title -
international journal of research - granthaalayah
Language(s) - English
Resource type - Journals
eISSN - 2394-3629
pISSN - 2350-0530
DOI - 10.29121/granthaalayah.v6.i2.2018.1563
Subject(s) - particle swarm optimization , crossover , multi swarm optimization , mathematical optimization , convergence (economics) , premature convergence , swarm behaviour , metaheuristic , meta optimization , computer science , global optimization , local optimum , operator (biology) , population , optimization problem , electric power system , mathematics , power (physics) , artificial intelligence , physics , repressor , quantum mechanics , transcription factor , economics , gene , economic growth , demography , sociology , biochemistry , chemistry
In this paper, an Ameliorated Particle Swarm Optimization (APSO) algorithm has been proposed to solve the optimal reactive power dispatch problem. Particle Swarm Optimization (PSO) is swarm intelligence-based exploration and optimization algorithm which is used to solve global optimization problems. But due to deficiency of population diversity and early convergence it is often stuck into local optima. Diversity upsurges and avoids premature convergence by using evolutionary operators in PSO. In this paper the intermingling crossover operator is used to upsurge the exploration capability of the swarm in the exploration space. Particle Swarm Optimization uses this crossover method to converge optimum solution in quick manner. Thus the intermingling crossover operator is united with particle swarm optimization to augment the performance and possess the diversity which guides the particles to the global optimum powerfully. Proposed Ameliorated Particle Swarm Optimization (APSO) algorithm has been tested in standard IEEE 30 bus test system and simulation results shows clearly the improved performance of the projected algorithm in reducing the real power loss and static voltage stability margin has been enhanced.