基于时频相似度的油纸绝缘多局部放电源脉冲群分离与识别策略

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (979 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Multiple partial discharge(PD) sources are often generated within power transformers due to the complexity of liquid-solid insulation system and operation condition,which would give rise to crossover and overlap of the registered PRPD patterns and incorrect diagnosis. To solve the problem of multiple PD sources recognition,a new method based on time-frequency analysis(TFA) of PD pulses and affinity propagation clustering(APC) is proposed for pulses separation and recognition of multiple PD sources of oil-paper insulation in transformers. The multiple PD pulses are firstly separated by input the S transform(ST) based time-frequency similarity matrix into affinity propagation clustering(APC) algorithm. Then,a support vector machine with particle swarm optimization(PSO-SVM) classifier based on PRPD statistical features is employed to obtain the recognition results of PRPD sub-patterns relevant to each PD source,and thereby examine the separation effectiveness. The PD data of artificial defect models acquired in laboratory are adopted for algorithms testing. It is shown that ST combined with APC can effectively eliminate pulse-shaped noises(PSN) and separate pulses of multiple PD sources.

Key words： Transformers oil-paper insulation multiple PD sources S transform affinity propagation clustering support vector machine

Received: 25 September 2013 Published: 22 January 2015

PACS:

TM835

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Wang Ke
	Li Jinzhong
	Zhang Shuqi
	Liao Ruijin
	Zhu Jie
	Wu Feifei

Cite this article:

Wang Ke,Li Jinzhong,Zhang Shuqi等. Pulses Separation and Recognition Strategy for Multiple Partial Discharge Sources of Oil-Paper Insulation Based on Time-Frequency Similarity[J]. Transactions of China Electrotechnical Society, 2014, 29(12): 251-260.

URL:

http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2014/V29/I12/251

[1] Liao R J,Yang L J,Li J,et al. Aging condition assessment of transformer oil-paper insulation model based on partial discharge analysis[J]. IEEE Transac- tions on Dielectrics and Electrical Insulation,2011,18(1): 303-311.
[2] Mazzetti C,Mascioli F M F,Baldini M,et al. Partial discharge pattern recognition by neuro-fuzzy networks in heat-shrinkable joints and terminations of XLPE insulated distribution cables[J]. IEEE Transactions on Power Delivery,2006,13(4): 1035-1044.
[3] Swedan A,EI-Hag A H,Assaleh K,et al. Acoustic detection of partial discharge using signal processing and pattern recognition techniques[J]. Insight,2012,54(12): 667-672.
[4] Li J,Jiang T Y,Harrison R F,et al. Recognition of ultra high frequency partial discharge signals using multi-scale features[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2012,19(4): 1412-1420.
[5] Chang W Y. Application of fuzzy C-means clustering approach and genetic algorithm to partial discharge pattern recognition[J]. International Review of Electrical Engineering,2012,7(4): 5213-5220.
[6] Chang H C,Kuo Y P,Lin H W. Partial discharge recognition system for current transformer using neural network and 2D wavelet transform[J]. IEEJ Transactions on Electrical and Electronic Engineering,2012,7(2): 144-151.
[7] Contin A,Pastore S. Classification and separation of partial discharge signals by means of their auto- correlation function evaluation[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2009,16(6): 1609-1622.
[8] Contin A,Cavallini A,Montanari G C,et al. Digital detection and fuzzy classification of partial discharge signals[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2002,9(3): 335-348.
[9] Cavallini A,Montanari G C,Contin A,et al. A new approach to the diagnosis of solid insulation systems based on PD signal inference[J]. IEEE Electrical Insulation Magazine,2003,19(2): 23-30.
[10] Cavallini A,Contin A,Montanari G C,et al. Advanced PD inference in on-field measurement. part 1: noise rejection[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2003,10(2): 216-224.
[11] Contin A,Pastore S. Automatic separation of multiple PD sources using an amplitude-autocorrelation relation diagram[C]. Conference Record of IEEE International Symposium on Electrical Insulation,2012: 434-438.
[12] Kuljaca N,Meregalli S,Contin A,et al. Separation of multiple sources in PD measurements using an amplitude-frequency relation diagram[C]. Proceedings of the International Conference on Solid Dielectrics,2010,1207-1210.
[13] Hao L,Lewin P L,Hunter J A,et al. Discrimination of multiple PD sources using wavelet decomposition and principal component analysis[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2011,18(5): 1702-1711.
[14] Pinpart T,Judd M D. Differentiating between partial discharge sources using envelope comparison of ultra- high-frequency signals[J]. IET Science,Measurement & Technology,2010,4(5): 256-267.
[15] Tang J,Li W,Liu Y L. Blind source separation of mixed PD signals produced by multiple defects in GIS[J]. IEEE Transactions on Power Delivery,2010,25(1): 170-176.
[16] Koltunowicz W,Plath R. Synchronous multi-channel PD measurements[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2008,15(6): 1715-1723.
[17] Belkov A,Obralic A,Koltunowicz W,et al. Advanced approach for automatic PRPD pattern recognition in monitoring of HV assets[C]. Conference Record of IEEE International Symposium on Electrical Insulation,2010.
[18] Kraetge A,Hoek S,Rethmeier,et al. Advanced noise suppression during PD measurements by real-time pulse-waveform analysis of PD pulses and pulse- shaped disturbances[C]. Conference Record of IEEE International Symposium on Electrical Insulation,2010.
[19] Stockwell R G,Mansinha L,Lowe R P. Localization of the complex spectrum: the S transform[J]. IEEE Transactions on Signal Processing,1996,44(4): 998- 1001.
[20] Frey B J,Dueck D. Clustering by passing messages between data points[J]. Science,2007,315(5814): 972-976.
[21] James R E,Phung B T. Development of computer- based measurements and their application to PD pattern analysis[J]. IEEE Transactions on Dielectrics and Electrical Insulation,1995,2(5): 838-856.
[22] Gulski E,Krivda A. Neural networks as a tool for recognition of partial discharges[J]. IEEE Transactions on Electrical Insulation,1993,28: 984-1001．
[23] Birsen Y. Statistical pattern analysis of partial discharge measurements for quality assessment of insulation systems in high-voltage electrical machinery[J]. IEEE Transactions on Industry Applications,2004,40(6): 1579-1594.
[24] Chang C C,Lin C J. LIBSVM. http://www.csie.ntu. edu.tw/~cjlin/libsvm/index.html,2011.
[25] Eberhart R,J Kennedy. A new optimizer using particle swarm theory[C]. The 6th International Symposium on Micro Machine and Hum Science,1995: 39-43.
[26] Cavallini A,Montanari G C,Ciani F. Analysis of partial discharge phenomenon in paper-oil insulation systems as a basis for risk assessment evaluation[C]. Proceedings of the 2005 IEEE International Con- ference on Dielectric Liquids,2005: 241-244.
[27] Liao R J,Yan J M,Yang L J,et al. Study on the relationship between damage of oil-impregnated insulation paper and evolution of phase-resolved partial discharge patterns[J]. European Transactions on Electrical Power,2011,21(8): 2112-2124.
[28] Li J H,Si W R,Yao X,et al. Measurement and simulation of partial discharge in oil impregnated pressboard with an electrical aging process[J]. Measure- ment Science & Technology,2009,20(10): 105701.
[29] Hao J,Liao R J,Chen G,et al. Quantitative analysis ageing status of natural ester-paper insulation and mineral oil-paper insulation by polarization/depo- larization current[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2012,19(1): 188-199.
[30] 杨丽君,孙才新,廖瑞金,等. 油纸绝缘老化状态判别的局部放电特征量[J]. 电力系统自动化,2007,31(10): 55-60. Yang Lijun,Sun Caixin,Liao Ruijin,et al. Partial discharge features applied in aging condition discri- mination of oil-paper insulation[J]. Automation of Electric Power Systems,2007,31(10): 55-60.
[31] Del Casale M D,Schifani R,Testa L,et al．Partial discharge tests using CIGRE method II upon nanocom- posite epoxy resins[C]. IEEE International Conference on Solid Dielectrics,2007,341-344．
[32] A. Cavallini,X. L. Chen,G. C. Montanari,et al. Diagnosis of EHV and HV transformers through an innovative partial-discharge-based technique[J]. IEEE Transactions on Power Delivery,2010,25(2): 814- 824.
[33] Y Qian,F Yao,S Jia. Band selection for hyperspectral imagery using affinity propagation[J]. IET Computer Vision,2009,3(4): 213-222.
[34] Li X H,Su H Y,Chu J. Multiple model soft sensor based on affinity propagation,Gaussian process and Bayesian committee machine[J]. Chinese Journal of Chemical Engineering,2009,17(1): 95-99.
[35] Sun C Y,Wang C H,Song S,et al. A local approach of adaptive affinity propagation clustering for large scale data[C]. IEEE International Joint Conference on Neural Networks,2009: 2998-3002.