用谐波小波包变换法提取GIS局部放电信号多尺度特征参数

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (442 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Ultra-high frequency(UHF) method has been widely used in GIS partial discharge(PD) detection, and the feature extraction of UHF PD signal is important for the accurate identification of the GIS internal insulation defect types and guiding the repair work, but it’s still lack of effective feature extraction methods. Therefore, this paper takes advantage of the strict box-shaped spectral of harmonic wavelet and proposes a harmonic wavelet packet transform(HWPT) method for UHF PD feature information extraction. Multi-scale decomposition with HWPT is adopted to the UHF PD signal produced by the four typical discharge models in the laboratory, and the sub-band spectrum aliasing and energy leaks of real wavelet packet decomposition is overcome. Using the energy and complexity difference of the UHF PD signal in different scales, the multi-scale energy and multi-scale sample entropy are extracted as the parameters for pattern recognition, so the time-frequency domain information of the UHF PD signal could be described more accurately. The classification and recognition results with support vector machine shows that this method has better recognition result than the real wavelet packet, and the multi-scale energy and multi-scale sample entropy feature parameters are both able to identify the four kinds of insulation defects effectively.

Key words： Harmonic wavelet packet feature extraction partial discharge spectrum aliasing multi-scale energy multi-scale sample entropy

Received: 03 April 2013 Published: 13 April 2015

PACS:

TM835

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Tang Ju
	Fan Lei
	Zhang Xiaoxin
	Liu Xin

Cite this article:

Tang Ju,Fan Lei,Zhang Xiaoxin等. Multi-Scale Feature Parameters Extraction of GIS Partial Discharge Signal with Harmonic Wavelet Packet Transform[J]. Transactions of China Electrotechnical Society, 2015, 30(3): 250-257.

URL:

http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2015/V30/I3/250

[1] 钱勇, 黄成军, 江秀臣, 等. 基于超高频法的GIS局部放电在线监测研究现状及展望[J]. 电网技术, 2005, 29(1): 40-43. Qian Yong, Huang Chengjun, Jiang Xiuchen, et al. Present situation and prospect of ultrahigh frequency method based research of on-line monitoring of partial discharge in gas insulated switchgear[J]. Power System Technology, 2005, 29(1): 40-43.
[2] 王辉, 郑文栋, 黄成军, 等. GK模糊分类算法在GIS局部放电模式识别中的应用[J]. 电力系统保护与控制, 2011, 39(17): 50-54. Wang Hui, Zheng Wendong, Huang Chengjun, et al. Application of Gustafson-Kessel fuzzy classification algorithm in the pattern recognition of partial discharge for GIS[J]. Power System Protection and Control, 2011, 39(17): 50-54.
[3] 姚林鹏, 徐颖敏, 钱勇, 等. 基于关联规则的XLPE电缆局部放电模糊识别研究[J]. 电工技术学报, 2012, 27(5)：92-98. Yao Linpeng, Xu Yingmin, Qian Yong, et al. Fuzzy pattern recognition of partial discharge in XLPE cable based on association rule[J]. Transactions of China Electrotechnical Society, 2012, 27(5): 92-98.
[4] 姚林鹏, 王辉, 钱勇, 等. 基于半监督学习的XLPE电缆局部放电模式识别研究[J]. 电力系统保护与控制, 2011, 39(14): 40-46. Yao Linpeng, Wang Hui, Qian Yong, et al. Pattern recognition of partial discharge in XLPE cable based on semi supervised learning[J]. Power System Protection and Control, 2011, 39(14): 40-46.
[5] 张晓星, 唐炬, 孙才新, 等. 基于多重分形维数的GIS局部放电模式识别[J]. 仪器仪表学报, 2007, 28(4): 597-602. Zhang Xiaoxing, Tang Ju, Sun Caixin, et al. PD pattern recognition based on multi-fractal dimensions in GIS[J]. Chinese Journal of Scientific Instrument, 2007, 28(4): 597-602.
[6] 任先文, 薛雷, 宋阳, 等. 基于分形特征的最小二乘支持向量机局部放电模式识别[J]. 电力系统保护与控制, 2011, 39(14): 143-147. Ren Xianwen, Xue Lei, Song Yang, et al. The pattern recognition of partial discharge based on fractal characteristics using LS-SVM[J]. Power System Protection and Control, 2011, 39(14): 143-147.
[7] 唐炬, 魏刚, 李伟, 等. 基于双向二维最大间距准则的局部放电灰度图像特征提取[J]. 电网技术, 2011, 35(3): 129-134. Tang Ju, Wei Gang, Li Wei, et al. Partial discharge gray image feature extraction based on Bi-directional Two-Dimensional maximum margin criterion[J]. Power System Technology, 2011, 35(3): 129-134.
[8] 刘云鹏, 律方成, 李成榕. 局部放电灰度图像数学形态谱的研究[J]. 中国电机工程学报, 2004, 24(5): 179-183. Liu Yunpeng, Lü Fangcheng, Li Chengrong. Study on pattern spectrum of partial discharge grayscale image [J]. Proceedings of the CSEE, 2004, 24(5): 179-183.
[9] Chang C, Chang C S, Jin J, et al. Source classification of partial discharge for gas insulated substation using waveshape pattern recognition[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2005, 12(2): 374-386.
[10] 李剑, 王小维, 金卓睿, 等. 变压器局部放电超高频信号多尺度网格维数的提取与识别[J]. 电网技术, 2010, 34(2): 159-163. Li Jian, Wang Xiaowei, Jin Zhuorui, et al. Multi-scale grid dimension extraction and recognition of ultra-high frequency signals of transformer partial discharge[J]. Power System Technology, 2010, 34(2): 159-163.
[11] 唐炬, 谢颜斌, 周倩, 等. 基于最优小波包变换与核主分量分析的局部放电信号特征提取[J]. 电工技术学报, 2010, 25(9): 35-40. Tang Ju, Xie Yanbin, Zhou Qian, et al. Feature extraction for partial discharge signals based on the optimal wavelet packet basis transform and kernel principal component analysis[J]. Transactions of China Electrotechnical Society, 2010, 25(9): 35-40.
[12] 李辉, 丁桦. 一种抗混叠和失真的小波包信号分解与重构算法[J]. 科学技术与工程, 2008, 8(20): 5580-5588. Li Hui, Ding Hua. Anti-aliasing and anti-distortion algorithm for signal decomposition and reconstruction based on wavelet package analysis[J]. Science Tech- nology and Engineering, 2008, 8(20): 5580-5588.
[13] Wang G, Yan Z, Hu X, et al. Classiﬁcation of surface EMG signals using harmonic wavelet packet transform [J]. Physiological measurement, 2006, 27(12): 1255- 1267.
[14] Yan R, Gao R X. An efﬁcient approach to machine health diagnosis based on harmonic wavelet packet transform[J]. Robotics and Computer-Integrated Manu- facturing, 2005, 21(4): 291-301.
[15] Newland D E. Harmonic wavelet analysis[J]. Procee- dings of the Royal Society of London. 1993, 443(10): 203-205.
[16] Newland D E. Harmonic and musical wavelets[J]. Proceedings of the Royal Society of London. 1994, 443(10): 605-620.
[17] 王炳成, 任朝晖, 闻邦椿. 基于非线性多参数的旋转机械故障诊断方法[J]. 机械工程学报, 2012, 48(5): 63-69. Wang Bingcheng, Ren Chaohui, Wen Bangchun. Fault diagnoses method of rotating machines based on nonlinear multi-parameters[J]. Journal of Mechanical Engineering, 2012, 48(5): 63-69.
[18] 陈继开, 周志宇, 李浩昱, 等. 快速小波熵输电系统暂态信号特征提取研究[J]. 电工技术学报, 2012, 27(12): 219-225. Chen Jikai, Zhou Zhiyu, Li Haoyu, et al. Study of fast wavelet entropy’s application in feature extraction of transient signals in transmission line[J]. Transactions of China Electrotechnical Society, 2012, 27(12): 219- 225.
[19] Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy [J]. American Journal of Physiology-Heart and Circulatory Physiology, 2000, 278(6): H2039-H2049.
[20] Al-Angari H M, Sahakian A V. Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome[J]. IEEE Transactions on Biomedical Engineering, 2007, 54(10): 1900-1904.
[21] Alcaraz R, Rieta J J. A review on sample entropy applications for the non-invasive analysis of atrial fibrillation electrocardiograms[J]. Biomedical Signal Processing and Control, 2010, 5(1): 1-14.
[22] Pincus S M. Assessing serial irregularity and its implications for health[J]. Annals of the New York Academy of Sciences, 2001, 954(1): 245-267.
[23] 张晓星, 唐炬, 孙才新, 等. 基于核统计不相关最优鉴别矢量集的GIS局部放电模式识别[J]. 电工技术学报, 2008, 23(9): 111-117. Zhang Xiaoxing, Tang Ju, Sun Caixin, et al. PD pattern recognition based on kernel statistical uncorrelated optimum discriminant vectors in GIS[J]. Transactions of China Electrotechnical Society, 2008, 23(9): 111- 117.
[24] 唐炬, 欧阳有鹏, 王存超, 等. 模拟气体绝缘组合电器产生不同局部放电的试验装置研制[J]. 重庆大学学报, 2010, 33(11): 39-45. Tang Ju, Ouyang Youpeng, Wang Cunchao, et al. Study on the testing device for simulating the partial discharges of different defects in gas insulated switch- gear[J]. Chinese Journal of Chongqing University, 2010, 33(11): 39-45.
[25] 张晓星, 唐炬, 彭文雄, 等. GIS局部放电检测的微带贴片天线研究[J]. 仪器仪表学报, 2006, 27(12): 1595-1599. Zhang Xiaoxing, Tang Ju, Peng Wenxiong, et al. Study on the outer UHF microstrip patch antenna for partial discharge detection in GIS[J]. Chinese Journal of Scientific Instrument, 2006, 27(12): 1595-1599.
[26] Vapnik V N. 统计学习理论的本质[M]. 张学工, 译北京: 清华大学出版社, 2000.
[27] Hsu C W, Lin C J. A comparison of methods for multi- class support vector machines[J]. IEEE Transactions on Neural Networks, 2002, 13(2): 415-425.