基于 GA 优化 T－S 模糊神经网络的小电流接地故障选线新方法

摘要

In the small current grounding system , the single-phase grounding fault happens frequently .How to quick-ly and accurately find the fault line has been a key research topic , and this doesn ’ t get effective solution .This paper presents a new method offault line selectionforpower distribution network based on genetic algorithm ( GA) to optimize T-S fuzzy neural network .By adjustingthe fitness function of traditionalGA , initial parameters andweights are opti-mized firstly , and the gradient descent method is used to optimize the second time .The influence of T-S fuzzy neural network , the traditional GA optimization of T-S fuzzy neural network and differentnetwork structures to network perfor-manceare discussed .The results of the studyillustrate the new GA to optimize T-S fuzzy neural network is better than T-S fuzzy neural network and traditional GA to optimize T -S fuzzy neural network in the term of line selection effect , which can accurately , effectively , andreliably select the fault line .%在小电流接地系统中，发生最多的是单相接地故障，针对如何快速准确地查找故障线路一直都是重点研究课题，且没有得到有效的解决。本文提出一种基于遗传算法（ GA）优化T－S模糊神经网络的配电网故障选线新方法：通过调整传统GA的适应度函数，先对网络初始参数、权值进行一次优化后，使用梯度下降法进行二次优化的选线算法。讨论了T－S模糊神经网络，传统GA优化的T－S模糊神经网络及不同网络结构对网络性能的影响。研究结果表明新型GA优化T－S模糊神经网络的选线效果明显优于T－S模糊神经网络和传统GA优化T－S模糊神经网络，能够快速、准确、可靠的选取故障线路。