摘要
高压断路器作为在各种电力系统应用中起到安全保护作用的设备,其故障诊断研究具有重大的意义。针对传统的BP神经网络收敛速度慢、收敛精度不足的问题,提出一种改进麻雀搜索算法(logistic sparrow search algorithm,LSSA)对BP神经网络进行优化。该模型通过在麻雀算法初始化种群时引入Logistic混沌映射得到更合理的初始参数,并在位置更新与最优解更新中分别引入动态自适应权重、柯西变异策略和反向学习策略,使该模型对断路器故障分类诊断的平均准确率接近100%,这表明改进后的BP神经网络具有更高的正确率。
As a device that can widely play a role in security protection in various power system applications,high voltage circuit breaker has great significance for its fault diagnosis research.Aiming at the problems of slow convergence speed and insufficient convergence accuracy of traditional BP neural network,an improved sparrow search algorithm is proposed to optimize the BP neural network.The model obtains more reasonable initial parameters by introducing Logistic chaotic map when the sparrow algorithm initializes the population,and introduces dynamic adaptive weight,Cauchy mutation strategy and reverse learning strategy in position update and optimal solution update respectively,so that the average accuracy of the model for circuit breaker fault classification diagnosis is close to 100%,which indicates that the improved BP neural network has higher accuracy.
作者
贾浩
张莲
张尚德
赵梦琪
赵娜
黄伟
JIA Hao;ZHANG Lian;ZHANG Shangde;ZHAO Mengqi;ZHAO Na;HUANG Wei(School of Electrical and Electronic Engineering,Chongqing University of Technology,Chongqing 400054,China;Chongqing Energy Internet Engineering Technology Research Center,Chongqing 400054,China)
出处
《湖南电力》
2022年第5期36-41,47,共7页
Hunan Electric Power
基金
国家自然科学基金(61402063)。
关键词
高压断路器
BP神经网络
麻雀搜索算法
high voltage circuit breaker
BP neural network
sparrow search algorithm
