Partial Discharge Pulse Segmentation Based on Clustering by Fast Search and Find of Density Peaks
Zhu Yongli1, Jiang Wei1, Liu Gang1,2
1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy SourcesNorth China Electric Power University Baoding 071003 China; 2. School of Electrical and Information Engineering Guizhou Institute of TechnologyGuiyang 550003 China
Abstract：Partial discharge (PD) signal processing is the basis of insulation state evaluation of electric power equipment, and feature extraction is the key link of signal processing. The feature extraction includes two steps: partial discharge pulse segmentation and discharge feature extraction. Pulse segmentation extraction is the premise of subsequent feature extraction and fault classification of PD signals. In order to preserve discharge information as much as possible and reduce manual interference, a PD pulse segmentation method based on clustering algorithm is proposed in this paper. The wavelet decomposition algorithm is used for filtering, and the noise rejection ratio (NRR) is used to characterize the filtering effect. The method takes all the partial discharge half-wave pulses into consideration to calculate the energy of each half-wave which is the integral of squared instant value of the PD signal over time. Therefore, it can describe the process of partial discharge more accurately. The Otsu algorithm is used to calculate the energy threshold adaptively, and one density peak clustering algorithm, called as clustering by fast search and find of density peaks (DPC), is used to achieve automatic segmentation of PD pulses. Three types of partial discharge models are established in the laboratory. Data from 10 sets of corona discharge, 11 sets of suspension discharge and 30 sets of cone-plate discharge are collected to verify the method. The results have achieved a recognition rate of more than 80%, which is higher or equivalent to the existing similar algorithms, demonstrating the superiority of this method.
朱永利, 蒋伟, 刘刚. 基于密度峰值聚类算法的局部放电脉冲分割[J]. 电工技术学报, 2020, 35(6): 1377-1386.
Zhu Yongli, Jiang Wei, Liu Gang. Partial Discharge Pulse Segmentation Based on Clustering by Fast Search and Find of Density Peaks. Transactions of China Electrotechnical Society, 2020, 35(6): 1377-1386.
[1] 齐伟强, 李俭, 陈柏超, 等. 基于COMSOL的变压器中超声波传播特性[J]. 电工技术学报, 2015, 30(增刊2): 195-200. Qi Weiqiang, Li Jian, Chen Baichao, et al.Ultrasonic wave propagation characteristics of the transformer based on COMSOL[J]. Transactions of China Electrotechnical Society, 2015, 30(S2): 195-200. [2] 周加斌, 陈玉峰, 井雨刚, 等. 特高压变压器磁屏蔽接地线断裂引起局部放电异常的检测与分析[J]. 电力系统保护与控制, 2018, 46(3): 110-115. Zhou Jiabin, Chen Yufeng, Jing Yugang, et al.Detection and analysis of abnormal partial discharge caused by the fracture of magnetic shielding ground line in UHV transformer[J]. Power System Protection and Control, 2018, 46(3): 110-115. [3] 李臻, 罗林根, 盛戈皞, 等. 基于压缩感知的特高频局部放电定位法[J]. 电工技术学报, 2018, 33(1): 202-208. Li Zhen, Luo Lingen, Sheng Gehao, et al.Ultrahigh frequency partial discharge localization methodology based on compressed sensing[J]. Transactions of China Electrotechnical Society, 2018, 33(1): 202-208. [4] 司文荣, 李军浩, 袁鹏, 等. 局部放电光测法的研究现状与发展[J]. 高压电器, 2008, 44(3): 261-264. Si Wenrong, Li Junhao, Yuan Peng, et al.Current situation and development of optical detection for partial discharge[J]. High Voltage Apparatus, 2008, 44(3): 261-264. [5] 张晓星, 董星辰, 陈秦川. 锐钛矿型(101)晶面吸附SF6局部放电分解组分的气敏机理分析[J]. 电工技术学报, 2017, 32(3): 200-209. Zhang Xiaoxing, Dong Xingchen, Chen Qinchuan.Gas sensing mechanism analysis of SF6 decomposed gases adsorption on anatase (101) surface under partial discharge[J]. Transactions of China Electro- technical Society, 2017, 32(3): 200-209. [6] 郭俊, 吴广宁, 张血琴, 等. 局部放电检测技术的现状和发展[J]. 电工技术学报, 2005, 20(2): 29-35. Guo Jun, Wu Guangning, Zhang Xueqin, et al.The actuality and perspective of partial discharge detection techniques[J]. Transactions of China Elec- trotechnical Society, 2005, 20(2): 29-35. [7] 程养春. 发电机定子绝缘局部放电非接触式在线监测方法的研究[D]. 北京: 华北电力大学, 2005. [8] Krivda A.Automated recognition of partial dis- charges[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 1995, 2(5): 796-821. [9] 贾亚飞, 朱永利, 王刘旺, 等. 基于VMD和多尺度熵的变压器内绝缘局部放电信号特征提取及分类[J]. 电工技术学报, 2016, 31(19): 208-217. Jia Yafei, Zhu Yongli, Wang Liuwang, et al.Feature extraction and classification on partial discharge signals of power transformers based on VMD and multiscale entropy[J]. Transactions of China Electro- technical Society, 2016, 31(19): 208-217. [10] 王刘旺, 朱永利, 贾亚飞, 等. 局部放电大数据的并行PRPD分析与模式识别[J]. 中国电机工程学报, 2016, 36(5): 1236-1244. Wang Liuwang, Zhu Yongli, Jia Yafei, et al.Parallel phase resolved partial discharge analysis for pattern recognition on massive PD data[J]. Proceedings of the CSEE, 2016, 36(5): 1236-1244. [11] Wei Gang, Tang Ju, Zhang Xiaoxing, et al.Gray intensity image feature extraction of partial discharge in high-voltage cross-linked polyethylene power cable joint[J]. International Transactions on Elec- trical Energy Systems, 2014, 24(2): 215-226. [12] 沈煜, 阮羚, 谢齐家, 等. 采用甚宽带脉冲电流法的变压器局部放电检测技术现场应用[J]. 高电压技术, 2011, 37(4): 937-943. Shen Yu, Ruan Ling, Xie Qijia, et al.On-site application of partial discharge detection of trans- former using very wide bandwidth pulse current method[J]. High Voltage Engineering, 2011, 37(4): 937-943. [13] 邱昌容, 王乃庆. 电工设备局部放电及其测试技术[M]. 北京: 机械工业出版社, 1994. [14] 唐志国, 唐铭泽, 李金忠, 等. 电气设备局部放电模式识别研究综述[J]. 高电压技术, 2017, 43(7): 2263-2277. Tang Zhiguo, Tang Mingze, Li Jinzhong, et al.Review on partial discharge pattern recognition of electrical equipment[J]. High Voltage Engineering, 2017, 43(7): 2263-2277. [15] 李剑. 局部放电灰度图象识别特征提取与分形压缩方法的研究[D]. 重庆: 重庆大学, 2001. [16] Lapp A, Kranz H G.The use of the CIGRE data format for PD diagnosis applications[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2000, 7(1): 102-112. [17] Lim Yun-Seog, Kim Sung-Hong, Ko Jae-Ho, et al.Identification of chaotic characteristics in partial discharge[C]//Dielectric Liquids, Proceedings of the IEEE 13th International Conference on Dielectric Liquids, Nara, Japan, 1999: 556-559. [18] Long Jiachuan, Wang Xianpei, Tian Meng, et al.A novel automatic pulse segmentation approach and its application in PD-induced electromagnetic wave detection[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2017, 24(1): 304-315. [19] Bajwa Abdullah Akram, Habib Salman, Kamran Muhammad.An investigation into partial discharge pulse extraction methods[J]. International Journal of Electrical Power & Energy Systems, 2015, 73: 964-982. [20] Darabad V P, Vakilian M, Blackburn T R, et al.An efficient PD data mining method for power transformer defect models using SOM technique[J]. International Journal of Electrical Power & Energy Systems, 2015, 71: 373-382. [21] Nagesh V, Gururaj B I.Automatic detection and elimination of periodic pulse shaped interferences in partial discharge measurements[J]. IEE Proceedings- Science, Measurement and Technology, 1994, 141(5): 335-342. [22] 刘明军. 变压器局放超高频监测与基于知识的分析方法研究[D]. 武汉: 华中科技大学, 2011. [23] 王鹏, 吴广宁, 罗杨, 等. 连续高压脉冲方波下局部放电测试系统设计[J]. 高电压技术, 2012, 38(3): 587-593. Wang Peng, Wu Guangning, Luo Yang, et al.Design of partial discharge testing system under continuous square pulse voltage[J]. High Voltage Engineering, 2012, 38(3): 587-593. [24] 万元, 李朝晖, 薛松, 等. 水轮发电机局部放电在线监测中的脉冲识别方法[J]. 高电压技术, 2009, 35(9): 2169-2175. Wang Yuan, Li Chaohui, Xue Song, et al.Pulses recognition method in partial discharge online monitoring of hydro-generators[J]. High Voltage Engineering, 2009, 35(9): 2169-2175. [25] 张安安, 何聪, 孙茂一, 等. 基于峭度和时域能量的局放脉冲提取算法[J]. 电子科技大学学报, 2017, 46(4): 559-564. Zhang Anan, He Cong, Sun Maoyi, et al.Partial discharge pulse extraction algorithm based on kurtosis and time domain energy[J]. Journal of University of Electronic Science and Technology of China, 2017, 46(4): 559-564. [26] 贾嵘, 赵佳佳, 武桦, 等. 相关概率小波变换在局部放电检测中的应用[J]. 高电压技术, 2017, 43(9): 2896-2902. Jia Rong, Zhao Jiajia, Wu Hua, et al.Application of correlated probabilistic wavelet transform in partial discharge detection[J]. High Voltage Engineering, 2017, 43(9): 2896-2902. [27] 张士宝, 董旭柱, 林渡, 等. 局部放电监测中现场干扰的分析与抑制[J]. 清华大学学报: 自然科学版, 1997, 37(8): 109-112. Zhang Shibao, Dong Xuzhu, Lin Du, et al.Analysis and suppression of field disturbance in partial discharge monitoring[J]. Journal of Tsinghua University: Sci & Tech, 1997, 37(8): 109-112. [28] 王刘旺, 朱永利, 李莉, 等. 基于自适应双阈值的局部放电基本参数提取[J]. 高电压技术, 2016, 42(4): 1268-1274. Wang Liuwang, Zhu Yongli, Li Li, et al.Extraction of fundamental parameters in partial discharge based on adaptive dual threshold[J]. High Voltage Engin- eering, 2016, 42(4): 1268-1274. [29] Otsu Nobuyuki.A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66. [30] 许向阳, 宋恩民, 金良海. Otsu准则的阈值性质分析[J]. 电子学报, 2009, 37(12): 2716-2719. Xu Xiangyang, Song Enming, Jin Lianghai.Charac- teristic analysis of threshold based on Otsu criterion[J]. Acta Electronica Sinica, 2009, 37(12): 2716-2719. [31] 高诗莹, 周晓锋, 李帅. 基于密度比例的密度峰值聚类算法[J]. 计算机工程与应用, 2017, 53(16): 10-17. Gao Shiying, Zhou Xiaofeng, Li Shuai.Clustering by fast search and find of density peaks based on density-raito[J]. Computer Engineering and Appli- cations, 2017, 53(16): 10-17. [32] Rodriguez Alex, Laio Alessandro.Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191): 1492-1496. [33] Glowacz Adam.DC motor fault analysis with the use of acoustic signals, coiflet wavelet transform, and K-nearest neighbor classifier[J]. Archives of Acoustics, 2015, 40(3): 321-327. [34] 肖俊安. 基于稀疏分解算法的局部放电信号干扰抑制技术的学习与思考[D]. 武汉: 华中科技大学, 2016. [35] Chang Dah-Chung, Chu Feitao.Feedforward active noise control with a new variable tap-length and step-size filtered-x LMS algorithm[J]. IEEE/ACM transactions on audio, speech, and language processing, 2014, 22(2): 542-555.
2020, Vol. 35 
