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作 者:赵莉华[1] 冀一玮 吴月峥 吴迅 宁文军 黄小龙 任俊文 ZHAO Lihua;JI Yiwei;WU Yuezheng;WU Xun;NING Wenjun;HUANG Xiaolong;REN Junwen(School of Electrical Engineering,Sichuan University,Chengdu 610065,China)
出 处:《电测与仪表》2024年第9期48-55,84,共9页Electrical Measurement & Instrumentation
基 金:国网黑龙江电力有限公司检修公司科技项目(SGHLJG00YJJS1400303);中央高校基本科研业务费专项资金资助项目(YJ202070)。
摘 要:BP(back propagation)神经网络由于具有线性映射能力强及自适应能力强等优点,常被用于高压断路器弹簧操作机构的故障诊断中,但易陷入局部最小点限制了网络的收敛速度和分类精确度。文中提出了一种基于L-M算法优化BP神经网络的高压断路器操作机构故障诊断方法,分析了神经网络的数学模型及映射关系,运用L-M算法对传统BP网络进行优化,解决了传统BP神经网络梯度下降法存在局部最小化、易产生平坦区等问题,有效地提高了算法的训练速度,同时提高了分类的精确度。诊断结果表明:L-M算法优化后的BP神经网络能有效地实现高压断路器操作机构故障诊断。文中研究内容对高压断路器操作机构故障诊断提供了思路与方法,对提高高压断路器安全可靠性具有重要意义。BP neural network is often used in fault diagnosis of spring operating mechanism of high voltage circuit breaker because of its strong linear mapping ability and self-adaptive ability.However,it is easy to fall into local minimum,which limits the convergence speed and classification accuracy of the network.This paper presents a fault diagnosis method of high voltage circuit breaker operating mechanism based on L-M algorithm to optimize BP neural network,analyzes the mathematical model and mapping relationship of neural network,optimizes the traditional BP network by using L-M algorithm,solves the problems of local minimization and flat area in traditional BP neural network gradient descent method,and effectively improves the training speed of the algorithm,as well as the accuracy of classification.The diagnosis results show that the BP neural network optimized by L-M algorithm can effectively realize the fault diagnosis of high voltage circuit breaker operating mechanism.The research content of this paper provides ideas and methods for fault diagnosis of high voltage circuit breaker operating mechanism,which is of great significance to improve the safety and reliability of high voltage circuit breaker.
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