现代制造工程Issue(4)：11-16,6.DOI:10.16731/j.cnki.1671-3133.2017.04.003
AGA-BP神经网络的变压器分接开关机械故障诊断
Application of tap-changer fault diagnosis of transformer based on AGA-BP neural network
摘要
Abstract
The On-Load Tap Changer(OLTC) of transformer has a complex nonlinear relationship between the mechanical fault symptom and fault type,and the traditional BP neural network is used to diagnose with low accuracy,slowing convergence rate and easy to fall into local minimum value and so on.An Adaptive Genetic Algorithm (AGA) is proposed to optimize the BP neural network fault diagnosis.The weights and thresholds of BP neural networks based on adaptive genetic algorithm optimization,and then the optimized BP neural network is applied to the OLTC mechanical fault diagnosis.The simulation results show that the fault diagnosis model of BP neural network optimized by AGA algorithm is superior to the traditional BP neural network method.It can effectively improve the accuracy and speed of the mechanical fault diagnosis of OLTC.关键词
变压器分接开关/BP神经网络/遗传算法/故障诊断Key words
transformer tap changer/BP neural network/genetic algorithm/fault diagnosis分类
信息技术与安全科学引用本文复制引用
王福忠,石秀立..AGA-BP神经网络的变压器分接开关机械故障诊断[J].现代制造工程,2017,(4):11-16,6.基金项目
国家自然科学基金资助项目(61104079) （61104079）
河南省产学研基金资助项目(132107000027) （132107000027）
