配电网故障定位的免疫二进制萤火虫算法

Immune binary firefly algorithm for fault locationof distribution network

萤火虫算法(firefly algorithm,FA)全局搜索能力强、收敛快,在此基础上,该文引入记忆池和免疫算法,研究基于免疫二进制萤火虫算法(immune binary firefly algorithm,IBFA)的配电网故障区段定位方法。搭建IEEE33节点配电网仿真模型,模拟配电网线路发生单点与多点故障,在故障信息完整与发生畸变的情况下,使用IBFA与二进制粒子群算法分别进行故障定位仿真试验。结果表明,在网络发生2处故障且有2位信息畸变时,该文算法迭代计算10次后能找到最优解,定位出故障区段,而二进制粒子群算法迭代100次后仍未能找到最优解,IBFA有更好的容错性与收敛性。

Based on the characteristics of strong global search ability and fast convergence of firefly algorithm(FA),the memory pool and immune algorithm are introduced to study the fault location method of distribution network based on immune binary firefly algorithm(IBFA).The IEEE33-bus distribution network simulation model is built to simulate single-point and multi-point faults in the distribution network.In the case of complete fault information and distortion,IBFA and binary particle swarm optimization algorithm are respectively used to conduct simulation experiments on fault location.The results show that when there are 2 faults in the network and 2 bits of information are distorted,the algorithm can find the optimal solution after 10 iterations,and locate the faulty section,while the binary particle swarm optimization algorithm fails to find the optimal solution after 100 iterations,and IBFA has better fault tolerance and convergence.