基于免疫算法的配电网故障定位方法研究

提出一种基于免疫算法的配电网故障定位新方法。免疫算法依据个体的亲和度对个体进行评价,根据个体的亲和度和浓度选择进行交叉、变异的个体,增大了群体多样性,避免了算法快速陷入局部最优,同时进化过程建立记忆单元,保留部分最优解,以免交叉、变异过程后群体退化,保证算法终止时得到的结果是历代出现过的最高亲和度的个体。针对多电源开环运行的配电网,结合区域划分思想,通过判断变电站变压器低压侧开关的故障信息,提出一种提高故障定位速度的方法。仿真结果表明该算法可有效实现配电网的故障定位,并通过与遗传算法相对比,验证了免疫算法用于配电网故障定位的有效性及可靠性。

Fault location of distribution network based on immune algorithm

A new method of fault section location and isolation in distribution networks based on immune algorithm (IA) is presented. Immune algorithm gets individual evaluation from its affinity. Selection of the crossover and mutation individuals is not only based on its affinity but also concentration, which increases colony diversity, avoids partial optimization, and speeds up the rate of evolution. Besides, this method can establish memory units and retain part of the optimal solutions in order to avoid degeneration in the process of crossover and mutation and guarantee the result is the individual of the highest level of affinity when the algorithm is terminated. A method based on the idea of area division and fault alarm of the incoming break is proposed to improve the speed of location for open-loop operational distribution networks. Finally the simulation results show that the immune algorithm can realize the goal of fault location and the comparison results with genetic algorithm testify its validity and reliability.