电力系统保护与控制2024，Vol.52Issue(3)：152-160,9.DOI:10.19783/j.cnki.pspc.230960
基于SO-PAA-GAF和AdaBoost集成学习的高压断路器故障诊断
Fault diagnosis of high-voltage circuit breaker based on SO-PAA-GAF and AdaBoost ensemble learning
摘要
Abstract
Aiming at the low accuracy of high-voltage circuit breaker fault diagnosis under small samples and complex working conditions,a fault diagnosis method of high-voltage circuit breaker based on vibration signal processing and AdaBoost ensemble learning is proposed.First,the high-voltage circuit breaker test platform is built and the switching vibration signals are collected under 8 working conditions.Second,after absolute value processing of vibration signals,piecewise aggregate approximation(PAA)is used to do piecewise averaging,and Gramian angular field(GAF)is used to convert new output sequences into pictures.The Relief F method is used to sort the importance of the extracted high-dimensional image features.Finally,the retained important features are input into the AdaBoost ensemble learning model for fault diagnosis,and the snake optimization algorithm is used to determine the optimal PAA step size and the number of input classifier features to further improve the fault diagnosis accuracy.The comparison and analysis results with various signal processing methods and classification models indicate that picture signal and AdaBoost ensemble learning model can deal with vibration signal effectively and judge fault type accurately,which provides a new way to diagnose high-voltage circuit breaker fault accurately and reliably.关键词
高压断路器/振动信号处理/分段聚合近似/格拉姆角场/故障诊断Key words
high-voltage circuit breaker/vibration signal processing/piecewise aggregate approximation/Gramian angular field/fault diagnosis引用本文复制引用
司江宽,吐松江·卡日,范想,高文胜,朱炜..基于SO-PAA-GAF和AdaBoost集成学习的高压断路器故障诊断[J].电力系统保护与控制,2024,52(3):152-160,9.基金项目
This work is supported by the National Natural Science Foundation of China(No.52067021). 国家自然科学基金项目资助(52067021) （No.52067021）
新疆维吾尔自治区自然科学基金项目资助(2022D01C35) （2022D01C35）
新疆维吾尔自治区优秀青年科技人才培养项目资助(2019Q012) （2019Q012）
