基于压缩感知的特高频局部放电定位法

doi:10.19595/j.cnki.1000-6753.tces.160989

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (635 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Existing UHF partial discharge (PD) localization technology is mainly based on time delay algorithm which has expensive hardware cost and is hard to achieve. This paper proposed a compressed sensing based PD localization methodology which was based on received signal strength indicator (RSSI) fingerprinting localization algorithm. It has easy fulfilling features and good environmental adaptability. Firstly, the RSSI fingerprinting was built in the offline stage. Secondly, in the online stage, BP neural networks is used to achieve preliminary localization, then compressed sensing strategy is deployed to achieve more accurate localization. The filed test showed that the mean errors of PD localization by out proposed method is 0.2 m and 93.9% of errors is smaller than 1 meter. The test proved that proposed localization algorithm is accurate and has good practical value.

Key words： Partial discharge localization compressed sensing received signal strength indicator fingerprint neural networks

Received: 29 June 2016 Published: 16 January 2018

PACS:

TM855

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Li Zhen
	Luo Lingen
	Sheng Gehao
	Jiang Yong
	Jiang Xiuchen

Cite this article:

Li Zhen,Luo Lingen,Sheng Gehao等. Ultrahigh Frequency Partial Discharge Localization Methodology Based on Compressed Sensing[J]. Transactions of China Electrotechnical Society, 2018, 33(1): 202-208.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.160989 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I1/202

[1] Portugues I E, Moore P J, Glover I A, et al. RF-based partial discharge early warning system for air-insulated substations[J]. IEEE Transactions on Power Delivery, 2009, 24(1): 20-28.
[2] 汪可, 廖瑞金, 王季宇, 等. 局部放电UHF脉冲的时频特征提取与聚类分析[J]. 电工技术学报, 2015, 30(2): 211-219. Wang Ke, Liao Ruijin, Wang Jiyu, et al. Time-frequency features extraction and clustering analysis of partial discharge UHF pulses[J]. Transactions of China Electrotechncial Society, 2015, 30(2): 211-219.
[3] 乔胜亚, 周文俊, 唐念, 等. 不同吸附剂对GIS局部放电特征气体变化规律的影响[J]. 电工技术学报, 2016, 31(3): 113-120. Qiao Shengya, Zhou Wenjun, Tang Nian, et al. Effects of different adsorbents on the evolving law of target gases under partial discharges in GIS[J]. Transactions of China Electrotechncial Society, 2016, 31(3): 113-120.
[4] 侯慧娟, 盛戈皞, 孙岳, 等. 基于电磁波信号传播衰减模型的变电站局部放电定位方法[J]. 电工技术学报, 2014, 29(6): 326-332. Hou Huijuan, Sheng Gehao, Sun Yue, et al. The localization method of partial discharge in substation based on propagation and attenuation model of electromagnetic signal[J]. Transactions of China Electrotechncial Society, 2014, 29(6): 326-332.
[5] Zhang Rongbao, Guo Jianguang, Chu Fuhuan, et al. Environmental-adaptive indoor radio path loss model for wireless sensor networks localization[J]. International Journal of Electronics and Communications (AEU), 2011, 65(12): 1023-1031.
[6] Iorkyase E T, Tachtatzis C, Atkinson R C, et al. Localisation of partial discharge sources using radio fingerprinting technique[C]//Antennas & Propagation Conference (LAPC), Loughborough, 2015: 1-5.
[7] 周沙, 景亮. 基于矩特征与概率神经网络的局部放电模式识别[J]. 电力系统保护与控制, 2016, 44(3): 98-102. Zhou Sha, Jing Liang. Pattern recognition of partial discharge based on moment features and probabilistic neural network[J]. Power System Protection and Control, 2016, 44(3): 98-102.
[8] David L D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4), 1289-1306.
[9] Candès E, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
[10] 马丹丹, 王晓茹. 基于小波模极大值的单端行波故障测距[J]. 电力系统保护与控制, 2009, 37(3): 55-59. Ma Dandan, Wang Xiaoru. Single terminal methods of traveling wave fault location based on wavelet modulus maxima[J]. Power System Protection and Control, 2009, 37(3): 55-59.
[11] Candes E. The restricted isometry property and its implications for compressed sensing[J]. Academic Des Sciences, 2006, 346(1): 592-598.
[12] Baraniuk R G. Compressive sensing[J]. IEEE Signal Processing Magazine, 2007, 24(4): 118-121.
[13] Tsaig Y, Donoho D. Extenstion of compressed sensing[J]. Signal Processing, 2006, 86(3): 549-571.
[14] Candes E J, Tao T. Near optimal signal recovery from random projections[J]. Universal Encoding Strategies, 2006, 52(12): 5406-5425.
[15] 马晓奇, 邵滨, 崔宇, 等. 电力线OFDM的压缩感知均衡消噪方法[J]. 电力系统保护与控制, 2014, 42(15): 113-116. Ma Xiaoqi, Shao Bin, Cui Yu, et al. Equalizing method of eliminating impulse noise for OFDM PLC system[J]. Power System Protection and Control, 2014, 42(15): 113-116.
[16] Mallat S O, Zhang Z. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415.
[17] Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666.
[18] Needell D, Tropp J A. CoSaMP: iterative signal recovery from incomplete and inacciirate samples[J]. Applied and Computational Harmonic Analysis, 2009, 26(3): 301-321.
[19] Dai W, Milenkovic O. Subspace pursuit for compressive sensing: closing the gap between performance and complexity[R]. Illinois University Atfurbana Chamapaign, 2008.
[20] Flgueiredo M A T, Nowak R D, Wright S J. Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 586-597.