摘要：In this paper, an Enhancedparticle swarm optimization algorithm (EPSO) has been proposed to solve the reactive power 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. We can upsurge diversity and avoid 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. The proposedEnhanced particle swarm optimization algorithm (EPSO) has been tested in standard IEEE 30, 57,118 bus test systems and simulation results shows clearly the improved performance of the projected algorithm in reducing the real power loss and control variables are well within the limits.
其他摘要：In this paper, an Enhancedparticle swarm optimization algorithm (EPSO) has been proposed to solve the reactive power 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. We can upsurge diversity and avoid 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. The proposedEnhanced particle swarm optimization algorithm (EPSO) has been tested in standard IEEE 30, 57,118 bus test systems and simulation results shows clearly the improved performance of the projected algorithm in reducing the real power loss and control variables are well within the limits. Keywords: Optimal Reactive Power, Transmission loss, intermingling crossover operator
