电测与仪表2016，Vol.53Issue(15)：13-16,32,5.
基于混沌优化粒子群 BP神经网络的电力变压器故障诊断
Fault diagnosis of power transformers based on chaos particle swarm optimization BP neural network
摘要
Abstract
In order to solve the fault diagnostic problems of power transformers, this paper proposes a BP neural net-work algorithm based on chaos particle swarm optimization.This algorithm combines chaos, particle swarm and the BP neural network, and obtains the optimal weights and the initial threshold value of BP neural network by using chaos particle swarm optimization algorithm, and then, it takes the network training and testing.The algorithm takes advan-tage of ergodicity and sensitivity of initial value of chaos to optimize the parameters of particle swarm algorithm, and introduces the precocious judgment mechanism of local convergence, besides that, in order to avoid the algorithm easi-ly falling into local convergence, the algorithm takes chaotic disturbance at the precocious state.The training and tes-ting examples suggest that CPSO-BP neural network algorithm has better effect in transformers fault diagnosis.关键词
混沌/粒子群/BP神经网络/变压器/故障诊断Key words
chaos/particle swarm/BP neural networks/power transformer/fault diagnosis分类
信息技术与安全科学引用本文复制引用
公茂法,柳岩妮,王来河,宋保业,钟文强..基于混沌优化粒子群 BP神经网络的电力变压器故障诊断[J].电测与仪表,2016,53(15):13-16,32,5.基金项目
山东省自然科学基金项目 （）
