矿井供电系统串联型故障电弧仿真分析及诊断方法
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摘要
因接触不良或机械损伤,煤矿供电系统会产生串联型故障电弧,串联型故障电弧是引发煤矿电气火灾的原因之一,目前缺少有效的检测手段,影响了井下的供电安全。井下可能存在瓦斯和煤尘等易燃易爆物质,不宜开展串联型故障电弧实验,无法获得大量故障电弧电流样本,加大了井下串联型故障电弧诊断工作的难度。为研究矿井供电系统串联型故障电弧的典型特征及诊断方法,论文首先在Mayr-Schwarz电弧数学模型的基础上,建立了矿井供电系统串联型故障电弧仿真模型,并结合实验结果对仿真模型的性能进行了评估;然后对矿井供电系统的采煤系统、胶带输送系统、泵房排水系统、照明系统的串联型故障电弧、过电压、单相接地、两相接地短路、两相短路、三相短路电气故障进行仿真分析、特征分析,以电流信号的过零点数、峰峰值、方差、峭度系数、裕度因子、谐波畸变率、单边功率谱频率方差、小波包系数能量熵、小波包系数峰峰值为特征量,建立了矿井供电系统串联型故障电弧特征参数数据库;最后综合比较决策树、K近邻、Bagged trees多分类模式识别方法在故障电弧诊断、选相及抗负载电流波动扰动、抗背景噪声扰动方面的性能,提出了K近邻矿井供电系统串联型故障电弧诊断方法。结果表明,建立的串联型故障电弧仿真模型能够用于仿真分析矿井供电系统串联型故障电弧,所建立的特征参数数据库能够反映矿井供电系统串联型故障电弧的典型特征,提出的K近邻串联型故障电弧诊断方法可用于矿井供电系统串联型故障电弧诊断及选相。
Because of bad contact or mechanical damage,the coal power supply system might produce series arc fault.Series arc fault is one of the causes of electrical fire in coal mines. There may be inflammable and explosive materials such as gas and coal dust in the underground. It is not suitable to carry out series arc fault experiment, and thus a lot of fault diagnosis samples cannot be obtained, which brings difficulties for arc fault diagnosis. In order to study the typical characteristics and diagnostic methods of series fault arcs in mine power supply system, a series arc fault simulation model based on the Mayr-Schwarz arc mathematical model was established for mine power supply system and the performance of this simulation model was evaluated in this paper. Then it simulated and analyzed the electrical faults such as series arc fault, over voltage, single phase ground, two phase ground short circuit, two phase short circuit, three phase short circuit in the coal mining system, belt conveyor system, pump house drainage system, lighting system of mine power supply system. Also, the series arc fault characteristic parameter database of mine power supply system was established by using Zero-crossing point, peak-to-peak value, variance, kurtosis coefficient, margin factor, harmonic distortion rate, unilateral power spectrum frequency variance, wavelet packet coefficient energy entropy, wavelet packet coefficient peak-to-peak value as feature variables. Finally, the paper comprehensively compared the performance of decision tree, K-nearest neighbor and Bagged trees multi-classification pattern recognition method in fault arc diagnosis, phase selection and anti-load current fluctuation disturbance, anti-background noise disturbance, and proposed series arc fault diagnosis method for K nearest neighbor mine power supply system. The results show that the established series arc fault simulation model can be used to simulate and analyze the series arc fault of mine power supply system and the database established in this paper can reflect the typical characteristics of series arc fault in mine power supply system. It also showed that K-nearest series arc fault diagnosis method can be used for the series arc fault diagnosis and phase selection of mine power supply system.
Because of bad contact or mechanical damage,the coal power supply system might produce series arc fault.Series arc fault is one of the causes of electrical fire in coal mines. There may be inflammable and explosive materials such as gas and coal dust in the underground. It is not suitable to carry out series arc fault experiment, and thus a lot of fault diagnosis samples cannot be obtained, which brings difficulties for arc fault diagnosis. In order to study the typical characteristics and diagnostic methods of series fault arcs in mine power supply system, a series arc fault simulation model based on the Mayr-Schwarz arc mathematical model was established for mine power supply system and the performance of this simulation model was evaluated in this paper. Then it simulated and analyzed the electrical faults such as series arc fault, over voltage, single phase ground, two phase ground short circuit, two phase short circuit, three phase short circuit in the coal mining system, belt conveyor system, pump house drainage system, lighting system of mine power supply system. Also, the series arc fault characteristic parameter database of mine power supply system was established by using Zero-crossing point, peak-to-peak value, variance, kurtosis coefficient, margin factor, harmonic distortion rate, unilateral power spectrum frequency variance, wavelet packet coefficient energy entropy, wavelet packet coefficient peak-to-peak value as feature variables. Finally, the paper comprehensively compared the performance of decision tree, K-nearest neighbor and Bagged trees multi-classification pattern recognition method in fault arc diagnosis, phase selection and anti-load current fluctuation disturbance, anti-background noise disturbance, and proposed series arc fault diagnosis method for K nearest neighbor mine power supply system. The results show that the established series arc fault simulation model can be used to simulate and analyze the series arc fault of mine power supply system and the database established in this paper can reflect the typical characteristics of series arc fault in mine power supply system. It also showed that K-nearest series arc fault diagnosis method can be used for the series arc fault diagnosis and phase selection of mine power supply system.
引文
[1]孙继平.基于物联网的煤矿瓦斯爆炸事故防范措施及典型事故分析[J].煤炭学报,2011,36(7):1172-1176.SUN Jiping. The accident prevention measure and analysis based on Internet of things on in the gas explosion of coal mines[J]. Journal of China Coal Society,2011,36(7):1172-1176.
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[7]王尧,韦强强,葛磊蛟,等.基于电弧电流高频分量的串联交流电弧故障检测方法[J].电力自动化设备,2017,37(7):191-197.WANG Yao, WEI Qiangqiang, GE Leijiao, et al. Series AC arc fault detection based on high-frequency components of arc current[J]. Electric Power Automation Equipment,2017,37(7):191-197.
[8]吴春华,徐文新,李智华,等.光伏系统直流侧故障电弧类型辨识及电路保护[J].中国电机工程学报,2017,37(17):5028-5036,5222.WU Chunhua,XU Wenxin,LI Zhihua,et al. Arc fault type identification and circuit protection in photovoltaic system[J]. Proceedings of the CSEE,2017,37(17):5028-5036,5222.
[9]郭凤仪,陈艳君,王智勇,等.基于WVD和OEW算法识别矿用栓接电缆电连接松动故障[J].煤炭学报,2015,40(9):2218-2224.GUO Fengyi,CHEN Yanjun,WANG Zhiyong,et al. Identification of electric connection looseness fault for mining bolt cable based on WVD and OEW algorithm[J]. Journal of China Coal Society,2015,40(9):2218-2224.
[10]郭凤仪,高洪鑫,王智勇,等.基于ST-SVD-PCA的串联故障电弧特征提取方法[J].煤炭学报,2018,43(3):888-896.GUO Fengyi,GAO Hongxin,WANG Zhiyong,et al. Feature extraction method of series fault arc based on ST-SVD-PCA[J]. Journal of China Coal Society,2018,43(3):888-896.
[11]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 diagnosis method based on fractal dimension and support vector machine[J]. Journal of Electrical Engineering,2016,31(2):70-77.
[12]刘官耕,杜松怀,苏娟,等.低压电弧故障防护技术研究与发展趋势[J].电网技术,2017,41(1):305-313.LIU Guangeng,DU Songhuai,SU Juan,et al. Research on LV arc fault protection and its development trends[J]. Power System Technology,2017,41(1):305-313.
[13] WALTER M, FRANCK C. Improved method for direct black-box arc parameter determination and model validation[J]. IEEE Transactions on Power Delivery,2014,29(2):580-588.
[14]李定启,程远平,王海峰,等.基于决策树ID3改进算法的煤与瓦斯突出预测[J].煤炭学报,2011,36(4):619-622.LI Dingqi,CHENG Yuanping,WANG Haifeng,et al. Coal and gas outburst prediction based on improved decision'tree ID3 algorithm[J]. Journal of China Coal Society,2011,36(4):619-622.
[15]付文龙,周建中,李超顺,等.基于模糊K近邻支持向量数据描述的水电机组振动故障诊断研究[J].中国电机工程学报,2014,34(32):5788-5795.FU Wenlong,ZHOU Jianzhong,LI Chaoshun,et al. Vibrant fault diagnosis for hydro-electric generating unit based on support vector data description improved with fuzzy K nearest neighbor[J].Proceedings of the CSEE,2014,34(32):5788-5795.
[16]宋华明,程龙生,马义中,等.基于集成学习算法的多变量质量诊断研究[J].数理统计与管理,2014,33(6):1048-1057.SONG Huaming,CHENG Longsheng,MA Yizhong,et al. Mulitivariate statistical process diagnosis based on ensembling learning algorthm[J]. Journal of Applied Statistics and Management,2014,33(6):1048-1057.
[2] PASUMPON S,SARAVANAN R,MARUTHU A,et al. Evaluation of high-voltage circuit breaker performance with modified schavemaker arc model[J]. International Journal of Digital Communication and Networks,2016,3(2):18-23.
[3] HOSSEINI S M H,EMANUEL Youhanna Eshagh,ALI Reza Edalatian. The modeling of electric arc in high voltage circuit breakers with use of schavemaker model and negative feedback[J]. International Journal of Science and Engineering Investigations, 2013,2(16):19-22.
[4] RONG M,LI M,WU Yi,et al. 3-D MHD modeling of internal fault arc in a closed container[J]. IEEE Trans. Power Del,2017,32(3):1220-1227.
[5]郭凤仪,王鑫,刘艳丽.基于Mayr模型改进的适用于串联故障电弧的新模型[J].电工电能新技术,2015,34(2):67-71.GUO Fengyi,WANG Xin,LIU Yanli. New improved model for series arc fault based on Mayr model[J]. Advanced Technology of Electrical Engineering and Energy,2015,34(2):67-71.
[6]姜斌峰,王莉.低压交流电线故障电弧模型研究[J].电力系统及其自动化学报,2009,21(4):20-24.JIANG Binfeng,WANG Li. Research on arc model in ac low-voltage electrical wire fault[J]. Proceedings of the CSU-EPSA, 2009,21(4):20-24.
[7]王尧,韦强强,葛磊蛟,等.基于电弧电流高频分量的串联交流电弧故障检测方法[J].电力自动化设备,2017,37(7):191-197.WANG Yao, WEI Qiangqiang, GE Leijiao, et al. Series AC arc fault detection based on high-frequency components of arc current[J]. Electric Power Automation Equipment,2017,37(7):191-197.
[8]吴春华,徐文新,李智华,等.光伏系统直流侧故障电弧类型辨识及电路保护[J].中国电机工程学报,2017,37(17):5028-5036,5222.WU Chunhua,XU Wenxin,LI Zhihua,et al. Arc fault type identification and circuit protection in photovoltaic system[J]. Proceedings of the CSEE,2017,37(17):5028-5036,5222.
[9]郭凤仪,陈艳君,王智勇,等.基于WVD和OEW算法识别矿用栓接电缆电连接松动故障[J].煤炭学报,2015,40(9):2218-2224.GUO Fengyi,CHEN Yanjun,WANG Zhiyong,et al. Identification of electric connection looseness fault for mining bolt cable based on WVD and OEW algorithm[J]. Journal of China Coal Society,2015,40(9):2218-2224.
[10]郭凤仪,高洪鑫,王智勇,等.基于ST-SVD-PCA的串联故障电弧特征提取方法[J].煤炭学报,2018,43(3):888-896.GUO Fengyi,GAO Hongxin,WANG Zhiyong,et al. Feature extraction method of series fault arc based on ST-SVD-PCA[J]. Journal of China Coal Society,2018,43(3):888-896.
[11]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 diagnosis method based on fractal dimension and support vector machine[J]. Journal of Electrical Engineering,2016,31(2):70-77.
[12]刘官耕,杜松怀,苏娟,等.低压电弧故障防护技术研究与发展趋势[J].电网技术,2017,41(1):305-313.LIU Guangeng,DU Songhuai,SU Juan,et al. Research on LV arc fault protection and its development trends[J]. Power System Technology,2017,41(1):305-313.
[13] WALTER M, FRANCK C. Improved method for direct black-box arc parameter determination and model validation[J]. IEEE Transactions on Power Delivery,2014,29(2):580-588.
[14]李定启,程远平,王海峰,等.基于决策树ID3改进算法的煤与瓦斯突出预测[J].煤炭学报,2011,36(4):619-622.LI Dingqi,CHENG Yuanping,WANG Haifeng,et al. Coal and gas outburst prediction based on improved decision'tree ID3 algorithm[J]. Journal of China Coal Society,2011,36(4):619-622.
[15]付文龙,周建中,李超顺,等.基于模糊K近邻支持向量数据描述的水电机组振动故障诊断研究[J].中国电机工程学报,2014,34(32):5788-5795.FU Wenlong,ZHOU Jianzhong,LI Chaoshun,et al. Vibrant fault diagnosis for hydro-electric generating unit based on support vector data description improved with fuzzy K nearest neighbor[J].Proceedings of the CSEE,2014,34(32):5788-5795.
[16]宋华明,程龙生,马义中,等.基于集成学习算法的多变量质量诊断研究[J].数理统计与管理,2014,33(6):1048-1057.SONG Huaming,CHENG Longsheng,MA Yizhong,et al. Mulitivariate statistical process diagnosis based on ensembling learning algorthm[J]. Journal of Applied Statistics and Management,2014,33(6):1048-1057.