基于小波包熵特征的污秽绝缘子放电故障识别

Jinyi Deng, F. Lin, Zitao Xu, Danrui Ma, Lijun Jin
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引用次数: 0

摘要

电气设备外绝缘缺陷的检测和类型识别是高压绝缘状态评估的重要组成部分。紫外脉冲检测技术是利用紫外传感器对放电进行检测和分析。本文提出了一种基于小波包变换熵值特征的污秽绝缘子放电缺陷识别方法。利用小波包变换对污染绝缘子放电的紫外脉冲信号进行分解重构,提取信号的三维时频分布进行时频分析,提取信号的时频域小波包熵特征,利用支持向量机(SVM)等分类器对电晕和电弧两种放电类型进行分类。为了验证本文提出的算法的有效性,在实验室搭建了表面放电测试装置,对陶瓷绝缘子的电晕放电和电弧放电类型进行了分类实验,并利用紫外脉冲传感器进行检测。结果表明,本文提出的特征提取方法对电晕和电弧两种放电状态的识别率高达95%。同时,该检测系统可实现外绝缘放电的长期在线检测,并可扩展到变电站和开关柜的检测中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discharge Fault Identification of Polluted Insulators Based on Wavelet Packet Transform Entropy Features
The detection and type identification of defects in the external insulation of electrical equipment is an important part of high-voltage insulation condition assessment. UV pulse detection technology is used to detect and analyze the discharges by using UV sensors. In this paper, we propose a method to identify the defects of polluted insulator discharge based on the entropy feature of wavelet packet transform. The wavelet packet transform is used to decompose and reconstruct the UV pulse signal of the polluted insulator discharge and extract the three-dimensional time-frequency distribution of the signal for time-frequency analysis, extract the wavelet packet entropy features of the signal in the time-frequency domain, and classify the two discharge types of corona and arc using classifiers such as support vector machine (SVM). In order to verify the effectiveness of the algorithm proposed in this paper, a surface discharge test device was set up in the laboratory, and experiments were conducted to classify the corona and arc discharge types of ceramic insulators, which were detected by UV pulse sensors. The results show that the recognition rate of identifying the two discharge states of corona and arc is as high as 95% using the feature extraction method proposed in this paper. Meanwhile, the detection system can realize long-term online detection of external insulation discharge and can be extended to substation and switchgear detection.
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