基于相空间重构与改进GSA-SVM的高压断路器机械故障诊断

doi:10.11930/j.issn.1004-9649.202004025

中国电力 ›› 2021, Vol. 54 ›› Issue (10): 169-176.DOI: 10.11930/j.issn.1004-9649.202004025

• 国家“十三五”智能电网重大专项专栏：（五）电力传感技术及应用专栏 • 上一篇下一篇

基于相空间重构与改进GSA-SVM的高压断路器机械故障诊断

夏小飞, 芦宇峰, 苏毅, 杨健

广西电网有限责任公司电力科学研究院，广西南宁 530023

收稿日期:2020-04-06 修回日期:2021-06-07 出版日期:2021-10-05 发布日期:2021-10-16
作者简介:夏小飞(1981-),男,硕士,高级工程师,从事开关技术研究,E-mail:23459133@qq.com;芦宇峰(1982-),男,博士,高级工程师,从事开关技术研究,E-mail:luyufenghust@163.com;苏毅(1988-),男,硕士,工程师,从事开关技术研究,E-mail:suyi935665054@126.com;杨健(1991-),男,硕士,工程师,从事开关技术研究,E-mail:hlyangjian@126.com
基金资助:
中国南方电网有限责任公司科技项目(基于振动特性分析的断路器故障诊断技术研究，GXKJXM20180905)

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Phase Space Reconstruction and Improved GSA-SVM

XIA Xiaofei, LU Yufeng, SU Yi, YANG Jian

Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning 530023, China

Received:2020-04-06 Revised:2021-06-07 Online:2021-10-05 Published:2021-10-16
Supported by:
This work is supported by Science and Technology Project of China Southern Power Grid Co., Ltd. (Research on Fault Diagnosis Technology of Circuit Breaker Based on Vibration Characteristic Analysis, No.GXKJXM20180905)

摘要/Abstract

摘要： 断路器机械部件传动、撞击产生的振动信号具有混沌特性，运用常规的信号处理方法很难分析其特性。首先采用互信息法和Cao算法将振动信号重构至高维空间后，计算其排列熵作为特征向量，输入支持向量机对断路器机械故障类型进行诊断，最后用粒子群算法（PSO）改进的万有引力搜索算法（GSA）混合算法优化支持向量机参数，利用断路器实测振动信号进行验证。结果表明：相空间重构与排列熵结合能够准确提取断路器振动信号的特征，采用PSO-GSA改进的支持向量机能快速有效分辨断路器故障类型，解决了现有诊断方法的路径扭曲、能量泄露和模态混叠等问题。

关键词: 断路器振动信号, 相空间重构, 排列熵, 万有引力搜索算法（GSA）, 支持向量机（SVM）

Abstract: The vibration signals generated by transmission and impact of circuit breaker mechanical components have chaotic characteristics, which are difficult to be analyzed with conventional signal processing methods. Firstly, the vibration signals are reconstructed into a high-dimensional space by mutual information method and Cao algorithm, and the permutation entropy is calculated as the feature vector. And then the support vector machine (SVM) is used to identify the mechanical fault types of circuit breakers. Finally, the PSO improved GSA hybrid algorithm is used to optimize the parameters of SVM, and the measured vibration signals of the circuit breakers are used to verify the results. The results show that the characteristics of circuit breaker vibration signals can be accurately extracted with combination of phase space reconstruction and permutation entropy. The PSO-GSA-SVM can quickly and effectively identify the fault types of circuit breakers, thus providing an effective solution to such problems as path distortion, energy leakage and mode overlap of existing diagnosis methods.

Key words: circuit breaker vibration signal, phase space reconstruction, permutation entropy, gravitational search algorithm (GSA), support vector machine (SVM)

夏小飞, 芦宇峰, 苏毅, 杨健. 基于相空间重构与改进GSA-SVM的高压断路器机械故障诊断[J]. 中国电力, 2021, 54(10): 169-176.

XIA Xiaofei, LU Yufeng, SU Yi, YANG Jian. Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Phase Space Reconstruction and Improved GSA-SVM[J]. Electric Power, 2021, 54(10): 169-176.

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基于相空间重构与改进GSA-SVM的高压断路器机械故障诊断

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Phase Space Reconstruction and Improved GSA-SVM

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基于相空间重构与改进GSA-SVM的高压断路器机械故障诊断

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