Adaptive Mutation Particle Swarm Optimization Algorithm Based On Population Diversity And Its Application

This paper studies about the PSO and the power system reactive power optimization problem. It is divided into five parts. In the part1, it introduces the status about the PSO theoretical research and application research firstly. And then it summaries the reactive power optimization research status, and points out the shortages when the intelligent algorithms are applicated in reactive power optimization. In the part2, it reviews the PSO principle and introduces the two classic improved PSO. And it analyse their convergences. In the part3, in order to overcome the disadvantage of the particle swarm optimization which easily falls into local optimum, an adaptive mutation disturbance particle swarm optimization algorithm based on personal best position (AMDPSO) is proposed. This algorithm is based on particle swarm optimization, and considers the disturbance. An adaptive criterion based on each particle’s personal best position is proposed in the algorithm. When the adaptive criterion is satisfied, the particles make the mutation based on its personal best position. Compared with the Inertia Weight Particle Swarm Optimization (IWPSO), Constriction Factor Particle Swarm Optimization(CFPSO) and Seeker Optimization Algorithm(SOA) in10test functions, the results show the AMDPSO has better optimization capability because it can make the particles escape from local optima easily and maintain the population diversity. In the part4, it describs the reactive power optimization principle and establish the mathematical model of the reactive power optimization. AMDPSO, IWPSO, CFPSO and SOA are applied for optimal reactive power in IEEE14-bus and IEEE30-bus power system. And then the results are compared and analysed. In the part5, it summaries the content of this paper and makes the outlook.