基于电流信号特征的弓网电弧识别方法

doi:10.19595/j.cnki.1000-6753.tces.161518

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Abstract Pantograph arc has become a serious hidden trouble to the safety operation of train. It’s of great significance to recognize timely pantograph arc for evaluating current collection quality, controlling arc and guiding maintenance. Current collection experiments under different conditions were carried out. The current collection status was divided into normal collection status and arc collection status. A kind of on-time recognition method of pantograph arc based on current signal and support vector machine (SVM) was proposed. An engineering implementation method was briefly introduced. The mean value, standard deviation and correlation coefficient of current were selected by the improved F-score (I-F-score) algorithm as typical characteristics of pantograph arc. The SVM model was established by using svmtrain function with Matlab software and the parameters of radial basis function were optimized by using grid search method. Lots of testing results showed that the suggested method can identify pantograph arc effectively. Some factors such as contact pressure, sliding speed, contact current, arc duration and sample rate have certain effects on recognition accuracy. Arc duration has larger effect on the recognition accuracy. The longer the arc duration, the higher the recognition accuracy is.

Key words： Pantograph arc improved F-score algorithm grid search support vector machine pattern recognition

Received: 14 September 2016 Published: 16 January 2018

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TM501

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	Wang Zhiyong
	Guo Fengyi
	Feng Xiaoli
	Wang Yuting
	Chen Cheng

Cite this article:

Wang Zhiyong,Guo Fengyi,Feng Xiaoli等. Recognition Method of Pantograph Arc Based on Current Signal Characteristics[J]. Transactions of China Electrotechnical Society, 2018, 33(1): 82-91.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.161518 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I1/82

[1] Barmada S, Landi A, Papi M, et al. Wavelet multiresolution analysis for monitoring the occurrence of arcing on overhead electrified railways[J]. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2003, 217(3): 177-187.
[2] Balestrino A, Bruno O, Giorgi P, et al. Electric welding effect: detection via phototube sensor and maintenance activities[C]//The World Congress on Railway Research, Koln, Germany, 2001: 19-23.
[3] Landi A, Menconi L, Sani L. Hough transform and thermo-vision for monitoring pantograph-catenary system[J]. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2006, 220(4): 435-447.
[4] Huang H H, Chen T H. Development of method for assessing the current collection performance of the overhead conductor rail systems used in electric railways[J]. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2008, 222(2): 159-168.
[5] Barmada S, Tucci M. Use of advanced signal processing techniques for arcing detection on AC pantograph catenary systems[C]//IEEE International Conference on Pantograph Catenary Interaction Framework for Intelligent Control, Amiens, France, 2011: 1-7.
[6] Barmada S, Raugi M, Tucci M, et al. Arc detection in pantograph-catenary systems by the use of support vector machines-based classification[J]. IET Electrical Systems in Transportation, 2013, 4(2): 45-52.
[7] Aydin I. A new approach based on firefly algorithm for vision-based railway overhead inspection system[J]. Measurement, 2015, 74: 43-55.
[8] Bruno O, Landi A, Papi M. Phototube sensor for monitoring the quality of current collection on overhead electrified railways[J]. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2001, 215(3): 231-241.
[9] 刘波. 电力机车弓网离线电弧检测装置[D]. 成都: 西南交通大学, 2005.
[10] 蒲文旭, 于龙, 陈唐龙, 等. 基于熵测度的地铁弓网燃弧电流扰动分析[J]. 高电压技术, 2014, 40(11): 3642 -3648. Pu Wenxu, Yu Long, Chen Tanglong, et al. Analysis of metro pantograph-catenary arc and current disturbance based on entropy measure[J]. High Voltage Engineering, 2014, 40(11): 3462-3468.
[11] 杨志鹏. 弓网电弧研究及其检测[D]. 北京: 北京交通大学, 2011.
[12] 王英, 刘志刚, 范福强, 等. 弓网电弧模型及其电气特性的研究进展[J]. 铁道学报, 2013, 35(8): 35-43. Wang Ying, Liu Zhigang, Fan Fuqiang, et al. Review of research development of pantograph-catenary arc model and electrical characteristics[J]. Journal of the China Railway Society, 2013, 35(8): 35-43.
[13] 郭凤仪, 张艳立, 王智勇, 等. 弓网电弧实验系统与辐射噪声实验研究[J]. 电工电能新技术, 2015, 34(12): 49-53. Guo Fengyi, Zhang Yanli, Wang Zhiyong, et al. Design of pantograph arc experiment system and study on radiated noise[J]. Advanced Technology of Electrical Engineering and Energy, 2015, 34(12): 49-53.
[14] 郭凤仪, 王喜利, 王智勇, 等. 弓网电弧辐射电场噪声实验研究[J]. 电工技术学报, 2015, 30(14): 220-225. Guo Fengyi, Wang Xili, Wang Zhiyong, et al. Research on radiated electric field noise of pantograph arc[J]. Transactions of China Electrotechnical Society, 2015, 30(14): 220-225.
[15] 郭凤仪, 王喜利, 王智勇, 等. 弓网离线接触电流总谐波畸变率的实验研究[J]. 电工技术学报, 2015, 30(12): 261-266. Guo Fengyi, Wang Xili, Wang Zhiyong, et al. Experimental research on total harmonic distortion of contact current caused by pantograph-catenary off-line[J]. Transactions of China Electrotechnical Society, 2015, 30(12): 261-266.
[16] EN50367-2006, railway applications-current collection systems-technical criteria for the interaction between pantograph and overhead line[S]. Brussels: European Committee for Electrotechnical Standardization, 2006.
[17] TB 10621-2009, 高速铁路设计规范(试行)[S]. 北京：中华人民共和国铁道部, 2009.
[18] 董昭德, 李岚. 接触网工程与设计[M]. 北京: 科学出版社, 2014.
[19] 王贺. 弓网系统接触压力动态特性研究[D]. 阜新: 辽宁工程技术大学, 2016.
[20] 吴广宁, 古圳, 高国强, 等. 弓网电弧形态特性试验研究[J]. 高电压技术, 2015, 41(11) : 3531-3537. Wu Guangning, Gu Zhen, Gao Guoqiang, et al. Experimental research on morphological characteristics of pantograph-catenary arc[J]. High Voltage Engineering, 2015, 41(11): 3531-3537.
[21] Jaganathan P, Rajkumar N, Kuppuchamy R. A comparative study of improved F-score with support vector machine and RBF network for breast cancer classification[J]. International Journal of Machine Learning and Computing, 2012, 2(6): 741-745.
[22] 杨凯, 张认成, 杨建红, 等. 基于分形维数和支持向量机的串联电弧故障诊断方法[J]. 电工技术学报, 2016, 31(2): 70-77. Yang Kai, Zhang Rencheng, Yang Jianhong, et al. Series arc fault diagnostic method based on fractal dimension and support vector machine[J]. Transactions of China Electrotechnical Society, 2016, 31(2): 70-77.
[23] 边莉, 边晨源. 电网故障诊断的智能方法综述[J]. 电力系统保护与控制, 2014, 42(3): 146-153. Bian Li, Bian Chenyuan. Review on intelligence fault diagnosis in power networks[J]. Power System Protection and Control, 2014, 42(3):146-153.
[24] 薛浩然, 张珂珩, 李斌, 等. 基于布谷鸟算法和支持向量机的变压器故障诊断[J]. 电力系统保护与控制, 2015, 43(8): 8-13. Xue Haoran, Zhang Keheng, Li Bin, et al. Fault diagnosis of transformer based on the cuckoo search and support vector machine[J]. Power System Protection and Control, 2015, 43(8): 8-13.
[25] 王小川. MATLAB神经网络43个案例分析[M]. 北京：航空航天大学出版社, 2013
[26] 王宁, 谢敏, 邓佳梁, 等. 基于支持向量机回归组合模型的中长期降温负荷预测[J]. 电力系统保护与控制, 2016, 44(3): 92-97. Wang Ning, Xie Min, Deng Jialiang, et al. Mid-long term temperature-lowering load forecasting based on combination of support vector machine and multiple regression[J]. Power System Protection and Control, 2016, 44(3): 92-97.