基于离散隐式马尔科夫模型的局部放电模式识别

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Abstract In this paper, the data sequences of apparent charge versus applied voltage (ΔQ-U) in the process of stepping-up/down voltage is used as characteristic features for pattern recognition of partial discharge (PD). Discrete hidden Markov models (DHMMs) classifier is introduced to realize the PD pattern recognition. Firstly, by utilizing vector quantization method, a codebook is formed based on LBG encoding data, and then the codebook index sequences are assigned to the train and test samples of various PD types respectively. In the training of the classifier, the DHMMs are obtained for each PD source. In the testing process, the output probabilities of the test samples in all DHMMs are calculated. The model number with the largest probability is chosen as the classification results. The recognition results from 5 PD sources and 150 samples demonstrate high classification rates and easy expansion of the classifier.

Key words： Partial discharge pattern recognition vector quantization discrete hidden Markov models

Received: 03 July 2009 Published: 07 March 2014

PACS:

TM835

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	Articles by authors
	Wang Ke
	Yang Lijun
	Liao Ruijin
	Qi Chaoliang
	Zhou Quan

Cite this article:

Wang Ke,Yang Lijun,Liao Ruijin等. Partial Discharge Pattern Recognition Based on Discrete Hidden Markov Models[J]. Transactions of China Electrotechnical Society, 2011, 26(8): 205-212.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2011/V26/I8/205

[1] Sahoo N C, Salama M M A, Bartnikas R. Trends in partial discharge pattern classification: a survey[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2005, 12(2): 248-264.
[2] 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.
[3] Filippidis A, Jain L C, Martin N M. Using genetic algorithms and neural networks for surface land mine detection[J]. IEEE Transactions on Signal Processing, 1999, 47(1): 176-186.
[4] Awad M, Chehdi K, Nasri A. Multicomponent image segmentation using a genetic algorithm and artificial neural network[J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(4): 571-575.
[5] 孙才新, 许高峰, 唐炬, 等. 以盒维数和信息维数为识别特征量的GIS局部放电模式识别方法[J]. 中国电机工程学报, 2005, 25(3): 100-104.
[6] 满玉岩, 高文胜, 高凯, 等. 发电机局部放电的统计特征识别[J]. 电工技术学报, 2006, 21(4): 41-45.
[7] Salama M M A, Bartnikas R. Determination of neural-network topology for partial discharge pulse pattern recognition[J]. IEEE Transactions on Neural Networks, 2002, 13(2): 446-456.
[8] Levent M A, John H L H. Selective training for Hidden Markov models with applications to speech classification[J]. IEEE Transactions on Speech and Audio Processing, 1999, 7(1): 46-54.
[9] Morguet P, Lang M. Spotting dynamic hand gestures in video image sequences using Hidden Markov Models[C]. Proceedings of ICIP, 1998, 3: 193-197.
[10] Pei Bingnan, Bao Zheng. Multi-aspect radar target recognition method based on scattering centers and HMMs classifiers[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(3): 1067-1074.
[11] 周星, 彭勤科, 王静波. 基于两层隐马尔可夫模型的入侵检测方法[J]. 计算机应用研究, 2008, 25(3): 911-914.
[12] 周凯, 吴广宁, 吴建东, 等. 基于局部放电统计参量的脉冲电压下绝缘老化分析[J]. 电工技术学报, 2008, 23(4): 6-12.
[13] 刘云鹏, 律方成, 李燕青, 等. 基于IFS的局部放电超声信号的数据压缩和模式识别[J]. 电工技术学报, 2003, 18(6): 93-97.
[14] 淡文刚, 陈祥训, 郑健超. 采用小波分析与神经网络技术的局部放电统计识别方法[J]. 中国电机工程学报, 2002, 22(9): 1-5.
[15] 高凯, 谈克雄, 李福祺, 等. 利用矩特征进行发电机线棒模型的局部放电模式识别[J]. 电工技术学报, 2001, 16(4): 61-64.
[16] 王猛, 谈克雄, 高文胜, 等. 局部放电脉冲波形的时频联合分析特征提取方法[J]. 电工技术学报, 2002, 17(2): 76-79.
[17] Li Jian, Sun Caixin, Gryzbowski S. Partial discharge image recognition influenced by fractal image compression[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2008, 15(2): 496-504.
[18] 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.
[19] 陆汝华, 杨胜跃, 朱颖, 等. 基于DHMM的轴承故障音频诊断方法[J]. 计算机工程与应用, 2007, 43(17): 218-220.
[20] Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition[J]. Proceedings of IEEE, 1989, 77(2): 257-286.
[21] 杨丽君, 廖瑞金, 孙才新, 等. 矢量量化在局部放电模式识别中的应用[J]. 中国电机工程学报, 2009, 29(31): 122-127.
[22] Linde Y, Buzo A, Gray R M. An algorithm for vector quantizer design[J]. IEEE Transactions on Comm-unications, 1980, 28(1): 84-95.
[23] 胡宏梅. 若干矢量量化码书设计算法研究[D]. 苏州: 苏州大学, 2007.
[24] 毛颖科, 关志成, 王黎明, 等. 基于BP人工神经网络的绝缘子泄漏电流预测[J]. 中国电机工程学报, 2007, 27(27): 7-12.