基于自适应遗传量子粒子群算法的配电网故障定位

Fault Location of Distribution Network Based on Adaptive Genetic Quantum Particle Swarm Optimization Algorithm

针对配电网发生故障时会存在故障过电流信息缺失或漏报的情况,造成现有方法无法对故障区间进行准确定位的问题,提出了一种基于AGA-QPSO(自适应遗传量子粒子群算法)的配电网故障定位方法。AGA-QPSO算法采用实数编码实现AGA算法及QPSO算法的统一编码,在QPSO寻优过程中引入AGA的遗传、变异操作,并根据适应度值动态调整遗传变异概率,以提升算法性能。然后构建了适用于配电网的开关函数及评价函数。最后在IEEE 33节点配电网中进行了仿真测试,结果表明改进算法对于单重故障与多重故障,无论短路电流信息是否完备,均能在较少迭代次数下进行准确定位,具有一定容错性。

Overcurrent information missing or omission in the case of distribution network faults disenables accurate fault interval location using the existing methods.This paper proposes a fault location method for the distribution network based on AGA-QPSO(adaptive genetic algorithm quantum particle swarm optimization).AGA-QPSO uses real number coding to realize the unified coding of the QPSO algorithm and AGA algorithm.In the process of QPSO optimization,AGA genetic and mutation operations were introduced,and the probability of genetic mutation was dynamically adjusted according to the fitness value to improve the algorithm performance.Then,the switching function and evaluation function suitable for the distribution network were constructed.Finally,simulation testing was carried out in the IEEE 33-bus distribution network.The results show that the improved algorithm,with its preferable fault tolerance,can accurately locate the single fault and multiple faults with fewer iteration times regardless of shortcircuit current information completeness.