Diagnosis of Circuit Breaker Current Characteristics Based on LSSA-BP Neural Network

JIA Hao; ZHANG Lian; ZHANG Shangde; ZHAO Mengqi; ZHAO Na; HUANG Wei

doi:10.3969/j.issn.1008-0198.2022.05.006

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Diagnosis of Circuit Breaker Current Characteristics Based on LSSA-BP Neural Network

更新时间：2025-06-11

- Diagnosis of Circuit Breaker Current Characteristics Based on LSSA-BP Neural Network
- Vol. 42, Issue 5, Pages: 36-41(2022)
- 作者机构：
  
  1. 重庆理工大学电气与电子工程学院,重庆,400054
  2. 重庆市能源互联网工程技术研究中心,重庆,400054
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2022.05.006
  CLC： TM561
- Published：2022
- 稿件说明：
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贾浩, 张莲, 张尚德, 赵梦琪, 赵娜, 黄伟. 基于LSSA-BP神经网络的断路器分合闸电流特征诊断[J]. 湖南电力, 2022, 42(5): 36-41.

JIA Hao, ZHANG Lian, ZHANG Shangde, et al. Diagnosis of Circuit Breaker Current Characteristics Based on LSSA-BP Neural Network[J]. 2022, 42(5): 36-41.
贾浩, 张莲, 张尚德, 赵梦琪, 赵娜, 黄伟. 基于LSSA-BP神经网络的断路器分合闸电流特征诊断[J]. 湖南电力, 2022, 42(5): 36-41. DOI： 10.3969/j.issn.1008-0198.2022.05.006.

JIA Hao, ZHANG Lian, ZHANG Shangde, et al. Diagnosis of Circuit Breaker Current Characteristics Based on LSSA-BP Neural Network[J]. 2022, 42(5): 36-41. DOI： 10.3969/j.issn.1008-0198.2022.05.006.

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。

Abstract

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.

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references

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Related Institution

Jiangsu Key Laboratory of Power Transmission & Distribution Equipment Technology(Hohai University)

College of The IOT Engineering, Hohai University

State Grid Taicang Electric Power Supply Company, Taicang

State Grid Jiangsu Electric Power Co., Ltd., Nanjing Power Supply Branch Company

State Grid Jiangsu Electric Power Co., Ltd., EHV Branch Company

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