Feature Extraction Method for Partial Discharge Pattern in GIS Based on Time-frequency Analysis and Fractal Theory

被引：0
|
作者：
Chen J. [1 ]
Xu C. [1 ]
Li P. [1 ]
Shao X. [2 ]
Li C. [3 ]
机构：
[1] College of New Energy, China University of Petroleum (East China), Qingdao
[2] Electric Research Institute of State Grid Zhejiang Electric Power Company, Hangzhou
[3] Qingdao Ruinengda Electrical Technology Limited Company, Qingdao
来源：
Gaodianya Jishu/High Voltage Engineering | 2021年 / 47卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Energy distribution; Fractal theory; Partial discharge; Pattern recognition; Support vector machine; Wavelet transform;
D O I：
10.13336/j.1003-6520.hve.20200507002
中图分类号：
学科分类号：
摘要：
Partial discharge (PD) pattern recognition is a critical indicator for evaluating the insulation state of electrical equipment. PD signals can be obtained by ultra high frequency(UHF) sensor. However, traditional methods based on statistical parameters have the disadvantages of high dimension and invalid information for signal feature extraction. In this paper, we propose a feature extraction method based on time-frequency analysis and fractal theory for partial discharge pattern recognition of gas insulated switchgear (GIS). Firstly, the wavelet transform is applied to extract the energy map of time-frequency distribution of partial discharge signals. Then, the differential box counting (DBC) is used to extract the fractal dimensions features of the energy distribution map, and the linear discriminant analysis (LDA) is used to reduce the dimension of the feature vectors. Finally, the support vector machine(SVM) is employed to classify the partial discharge defects. To evaluate the effectiveness of the proposed method in this paper, four typical types of defects, namely protrusion discharge, free moving particle discharge, surface discharge, and floating electrode discharge, have been set up in the 252 kV GIS model chamber of the laboratory. Partial discharge signals have been obtained by UHF sensors and classified by the proposed method. The experimental results show that the recognition accuracy rate for all the four typical discharge types exceeds 96% by using the proposed feature extraction method, which is significantly better than the traditional methods based on statistical parameters for signal feature extraction. © 2021, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
引用
下载
收藏
页码：287 / 295
页数：8
相关论文
共 23 条
  • [1] ZHU Deheng, YAN Zhang, TAN Kexiong, Et al., The electrical equipment condition monitoring and fault diagnosis technology, pp. 3-5, (2009)
  • [2] QI B, LI C, XING Z, Et al., Partial discharge initiated by free moving metallic particles on GIS insulator surface: severity diagnosis and as- sessment, IEEE Transactions on Dielectrics and Electrical Insulation, 21, 2, pp. 766-774, (2014)
  • [3] LI Qingquan, LI Simeng, SI Wen, Et al., Analysis of the key problem about insulation condition assessment of oil-paper in power transformers based on partial discharge, High Voltage Engineering, 43, 8, pp. 2558-2565, (2017)
  • [4] WANG Yanbo, ZHU Mingxiao, SHAO Xianjun, Et al., Simulation and experimental research on S parameter characteristics of partial discharge UHF signals in GIS, High Voltage Engineering, 44, 1, pp. 234-240, (2018)
  • [5] LI Zhen, LUO Lingen, CHEN Jingde, Et al., Novel localization method for partial discharge based on utral-high frequency wireless sensor array, High Voltage Engineering, 45, 2, pp. 418-425, (2019)
  • [6] ZHU Mingxiao, XUE Jianyi, SHAO Xianjun, Et al., Waveform analysis and feature extraction of partial discharge UHF signals in gas insulated switchgear, High Voltage Engineering, 43, 12, pp. 4078-4087, (2017)
  • [7] CHEN Jingde, GAO Zhaoli, LUO Lingen, Et al., Error correction of UHF partial discharge location method based on BP network, High Voltage Engineering, 44, 6, pp. 2040-2047, (2018)
  • [8] LI G Y, WANG X H, LI X, Et al., Partial discharge recognition with a multi-resolution convolutional neural network, Sensors, 18, 10, pp. 3512-3539, (2018)
  • [9] LI X, WANG X, XIE D, Et al., Time-frequency analysis of PD-induced UHF signal in GIS and feature extraction using invariant moments, IET Science, Measurement & Technology, 12, 2, pp. 169-175, (2018)
  • [10] LI Ruipeng, HUANG Chao, ZHANG Wei, Et al., Research on separation and clustering method of multi-source PD signals for metal-clad switchgear, High Voltage Apparatus, 51, 10, pp. 110-116, (2015)
123