【摘要】 针对标准粒子群算法在求解过程中容易陷入局部最优解的问题,提出了一种将免疫算法的免疫信息处理机制和自我调节机制引入到标准粒子群优化算法的新型优化算法,即免疫粒子群优化算法。以分布式电源建设运行费用、有功网损和环境成本最小为多目标函数,建立了分布式电源接入配电网的规划模型,采用免疫粒子群算法对模型进行求解,最终得到分布式电源接入配电网的最优配置方案。以风力发电为例,对IEEE 33节点算例进行仿真分析,结果表明,免疫粒子群算法与标准粒子群优化算法和混沌粒子群算法相比,收敛速度快、收敛精度高。更多还原

【Abstract】 It is easy for basic particle swarm optimization algorithm to fall into the local optimal solution,to address this issue,a new type of optimization algorithm is proposed,which introducing the immune information processing mechanism and self-adjustment mechanism of immune algorithm into the basic particle swarm optimization algorithm.A planning model of distributed generation accessing to distribution network is built,which takes the minimum cost of construction and operating,power loss and environmental cost as objective function.A final planning of optimal allocation of distributed generation can be got by using IPSO to solve this model.Taking wind power generation as example,simulation analysis of the IEEE33 node example shows that the immune particle swarm algorithm is fast and precise compared with basic & chaos particle swarm algorithm.更多还原