电子测量与仪器学报

王非,徐伟.基于 Choi-Williams 分布和排列熵的开关柜局部放电类型识别[J].电子测量与仪器学报,2023,37(10):32-40

基于 Choi-Williams 分布和排列熵的开关柜局部放电类型识别

Partial discharge type identification of switchgear based on Choi-Williamsdistribution and permutation entropy

DOI：

英文关键词:partial discharge ultrasonic Choi-Williams distribution permutation entropy

基金项目:国家重点研发计划政府间/ 港澳台重点专项项目(2021YFE0105500)、国家自然科学基金(41605121)项目资助

作者	单位
王非	1. 南京信息工程大学气象灾害预报预警与评估协同创新中心，2. 南京信息工程大学江苏省气象探测与信息处理重点实验室
徐伟	1. 南京信息工程大学气象灾害预报预警与评估协同创新中心，2. 南京信息工程大学江苏省气象探测与信息处理重点实验室

Author	Institution
Wang Fei	1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, 2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & Technology
Xu Wei	1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, 2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & Technology

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中文摘要:

开关柜局部放电类型识别对了解绝缘状态并及时维护有着重要的指导意义。局部放电类型识别的关键在于提取局部放电信号的特征。提出一种 Choi-Williams 分布与排列熵相结合的局部放电超声信号的特征提取方法,利用 Choi-Williams 分布获得局部放电超声信号的时频特征,求解局部放电超声信号的排列熵,得到信号时间序列的复杂度特征量,与时域特征量组合成特征向量,使用粒子群算法优化的 BP 神经网络对放电信号进行分类识别。实测数据分析表明,该方法对放电类型识别的准确率达到了 96. 67%,相较于传统的分形和时频分析方法,分别提高了 11. 67%和 1. 67%。

英文摘要:

The identification of partial discharge type of switchgear has important guiding significance for understanding the insulation state and timely maintenance. The key to partial discharge type identification is to extract the characteristics of the partial discharge signal. A feature extraction method for partial discharge ultrasonic signals combining Choi-Williams distribution and permutation entropy is proposed, the time-frequency characteristics of partial discharge ultrasonic signals are obtained by using Choi-Williams distribution, the permutational entropy of partial discharge ultrasonic signals is solved, the complexity feature quantity of signal time series is obtained, the time domain and complexity features are composed into feature vectors, and the BP neural network optimized by particle swarm optimization is used to classify and identify discharge signals. The measured data analysis shows that the accuracy of the method for the identification of discharge type reaches 96. 67%, which is 11. 67% and 1. 67% higher than the traditional fractal and timefrequency analysis methods, respectively.

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