基于知识融合PSO的风光互补发电系统优化

Wind-light Generation System Optimization Based on Knowledge Fusion PSO Algorithm

为提高风光互补发电系统的可靠性,减少其运行成本,研究基于知识融合粒子群算法(PSO)的风光互补发电系统优化配置,优化目标为最小化系统的安装成本,约束条件为供电可靠性。首先,针对粒子群算法易局部收敛的缺陷,利用混沌局部PSO搜索算法改善其收敛性;然后,若粒子未跳出局部最优,将粒子群进行简单聚簇,根据簇中心的位置细致搜索全局最优粒子,优化目标函数值组成的种群;最后,将所提方法应用到5个Benchmark测试函数及风光互补发电系统的优化配置中,实验结果表明了所提方法的有效性和实用性。

In order to improve the reliability of wind-light generation system and reduce the operation cost, a configured optimization method of wind-light generation system based on knowledge fusion particle swarm optimization(PSO) algorithm is presented, in which the optimization target is minimizing system installing cost and the constraint condition is power supply reliability. Firstly, in allusion to the limitation of local convergence of particle swarm optimization, using chaotic local PSO algorithm to improve its convergence;Then, clustering particle swarm using a simple method if the particle did not jump out of local optimum, and searching the global optimal particle meticulously according to the position of cluster center that optimizing the population which composed of the objective function value. Finally, the experimental results on 5 Benchmark test functions and optimizing configuration of wind-light generation system show that the effectiveness and applicability of the proposed method.