基于相关系数和偏态指标的航空串联电弧故障检测
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摘要
针对航空串联电弧故障的检测与识别问题,提出一种基于相关系数和偏态指标的航空电弧故障检测方法。通过提取不同负载电流信号的相关系数和偏态指标,构建二维特征量,分析对比了隐含层节点数对极限学习机性能的影响,引入灰狼优化的极限学习机进行分类识别。对阻性、阻感性、阻容性和非线性负载的大量实验结果表明,所提方法能够有效提取不同负载电弧故障特征,串联电弧故障诊断率高达98%,可为开发新型的航空电弧故障断路器提供可靠参考。
In response to the problem of detecting and identifying aviation series arc fault, a detection method based on correlation coefficient and bias-normal distribution index was proposed. By extracting the correlation coefficient and the bias-normal distribution index of different load current signals, the two-dimensional characteristic quantity was constructed, the influence of the number of hidden layer nodes on the performance of extreme learning machine was analyzed and compared, and the extreme learning machine for grey wolf optimization algorithm was introduced to classify and identify. Through a large number of experiments on resistive, inductive, capacitive and non-linear loads, the results show that the proposed method can effectively extract different load arc fault characteristics, and the series arc fault diagnosis rate is as high as 98%, which can provide reliable reference for developing new type of aviation arc fault detection device.
In response to the problem of detecting and identifying aviation series arc fault, a detection method based on correlation coefficient and bias-normal distribution index was proposed. By extracting the correlation coefficient and the bias-normal distribution index of different load current signals, the two-dimensional characteristic quantity was constructed, the influence of the number of hidden layer nodes on the performance of extreme learning machine was analyzed and compared, and the extreme learning machine for grey wolf optimization algorithm was introduced to classify and identify. Through a large number of experiments on resistive, inductive, capacitive and non-linear loads, the results show that the proposed method can effectively extract different load arc fault characteristics, and the series arc fault diagnosis rate is as high as 98%, which can provide reliable reference for developing new type of aviation arc fault detection device.
引文
[1] Nemir D, Martinez A, Diong B. Arc fault management using solid state switching[EB/OL]. https://www.sae.org/publications/technical-papers/content/2004-01-3197/. 2004-11-02.
[2] 郭凤仪,王鑫,刘艳丽(Guo Fengyi, Wang Xin, Liu Yanli).基于Mayr模型改进的适用于串联电弧故障的新模型(New improved model for series arc fault based on Mayr model)[J].电工电能新技术(Advanced Technology of Electrical Engineering and Energy),2015,34(2):67-71.
[3] 陈照,李奎,张洋子,等(Chen Zhao, Li Kui, Zhang Yangzi, et al.).基于电弧电磁辐射的故障电弧识别(Arc fault detection based on electromagnetic radiation)[J].电工电能新技术(Advanced Technology of Electrical Engineering and Energy),2017,36(3):70-74.
[4] 张国钢,宋政湘,柯春俊,等(Zhang Guogang, Song Zhengxiang, Ke Chunjun, et al.).基于电流高频分量时频特征的电弧故障检测方法(Arc fault detection method based on time frequency characteristic of high frequency current) [P]. 中国专利(Chinese Patent):200910218707.9.
[5] 程红,关晓晴,郭立东(Cheng Hong, Guan Xiaoqing, Guo Lidong).串联电弧故障信号的时频特征分析(Analysis on time frequency feature of series arc fault) [J].低压电器(Low Voltage Apparatus),2010,18(3):5-7.
[6] Yao X, Herrera L, Huang Y, et al. The detection of DC arc fault: Experimental study and fault recognition[A]. 27th Annual IEEE Applied Power Electronics Conference and Exposition [C]. Orlando, USA, 2012. 1720-1727.
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[8] Lee Weijen, Sahni M, Methaprayoon K, et al. A novel approach for arcing fault detection for medium low voltage switchgear[J]. IEEE Transactions on Industry Applications, 2009, 45(4):1475-1483.
[9] 王晓远,高淼,赵玉双(Wang Xiaoyuan, Gao Miao, Zhao Yushuang).阻性负载下低压故障电弧特性分析(Characteristic analysis of low voltage arc fault in resistive load conditions)[J].电力自动化设备(Electric Power Automation Equipment),2015,35(5):106-118.
[10] 唐金城,缪希仁,张丽萍(Tang Jincheng, Miao Xiren, Zhang Liping).基于DSP的低压配电线路电弧故障检测系统(Arc fault detection on low voltage distribution line based on DSP)[J].电工电能新技术(Advanced Technology of Electrical Engineering and Energy),2015,34(2):72-75.
[11] SAE AS5692: 2004, Arc Fault Circuit Breaker(AFCB), aircraft, trip-free single phase 115 VAC, 400Hz-constant frequency[S].
[12] UL Inc. UL standard for safety for arc fault circuit interrupters UL 1699[M]. Second Edition. Northbrook: Underwriter Laboratories Inc, 2006. 16-24.
[13] 郭庆丰(Guo Qingfeng).高阶统计量在滚动轴承故障诊断中的应用(High order statistics in rolling bearing fault Diagnosis) [D].西安:西安电子科技大学(Xi’an: Xi’an University of Electronic Science and Technology),2013.
[14] 刘德军,鲍光海,孙怀平(Liu Dejun, Bao Guanghai, Sun Huaping). 基于峰值指标和脉冲指标的串联电弧故障检测研究(Research on fault detection of series arc based on peak index and pulse index)[J].电气与能效管理技术(Electrical and Energy Management Technology),2017, (16):6-10, 38.
[15] 陈绍炜,柳光峰,冶帅,等(Chen Shaowei, Liu Guangfeng, Ye Shuai, et al.). 基于蝙蝠算法优化ELM的模拟电路故障诊断研究(Research for analog circuit fault diagnosis based on ELM optimized by bat algorithm)[J].电子测量技术(Electronic Measurement Technology),2015,38(2):138-141.
[16] 张英堂,马超尹,尹刚,等(Zhang Yingtang, Ma Chaoyin, Yin Gang, et al.).基于多极限学习机在线集成的柴油机故障诊断方法研究(Diesel engine fault diagnosis based on online ensemble of multiple extreme learning machine)[J].车用发动机(Vehicle Engine),2012,(6):85-89.
[17] 夏威,张麟,袁秋实,等(Xia Wei, Zhang Lin, Yuan Qiushi, et al.).改进极限学习机应用于电网故障诊断(Application of improved extreme learning machine in power system fault diagnosis)[J].电网与清洁能源(Power System and Clean Energy), 2015,31(4):15-19.
[18] 尹刚,张英堂,李志宁,等(Yin Gang, Zhang Yingtang, Li Zhining, et al.). 自适应集成极限学习机在故障诊断中的应用(Application of adaptive integrated limit learning machine in fault diagnosis)[J].振动、测试与诊断(Journal of Vibration, Testing and Diagnosis),2013,33(5):897-901.
[19] Mirjalili S, Lewis A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61.
[20] 白媛,陈京荣,展之婵(Bai Yuan, Chen Jingrong, Zhan Zhichan).改进灰狼优化算法的研究与分析(Research and analysis of improved gray wolf optimization algorithm)[J].计算机科学与应用(Computer Science and Application),2017,7(6):562-571.
[2] 郭凤仪,王鑫,刘艳丽(Guo Fengyi, Wang Xin, Liu Yanli).基于Mayr模型改进的适用于串联电弧故障的新模型(New improved model for series arc fault based on Mayr model)[J].电工电能新技术(Advanced Technology of Electrical Engineering and Energy),2015,34(2):67-71.
[3] 陈照,李奎,张洋子,等(Chen Zhao, Li Kui, Zhang Yangzi, et al.).基于电弧电磁辐射的故障电弧识别(Arc fault detection based on electromagnetic radiation)[J].电工电能新技术(Advanced Technology of Electrical Engineering and Energy),2017,36(3):70-74.
[4] 张国钢,宋政湘,柯春俊,等(Zhang Guogang, Song Zhengxiang, Ke Chunjun, et al.).基于电流高频分量时频特征的电弧故障检测方法(Arc fault detection method based on time frequency characteristic of high frequency current) [P]. 中国专利(Chinese Patent):200910218707.9.
[5] 程红,关晓晴,郭立东(Cheng Hong, Guan Xiaoqing, Guo Lidong).串联电弧故障信号的时频特征分析(Analysis on time frequency feature of series arc fault) [J].低压电器(Low Voltage Apparatus),2010,18(3):5-7.
[6] Yao X, Herrera L, Huang Y, et al. The detection of DC arc fault: Experimental study and fault recognition[A]. 27th Annual IEEE Applied Power Electronics Conference and Exposition [C]. Orlando, USA, 2012. 1720-1727.
[7] Müller P, Tenbohlen S, Maier R, et al. Characteristics of series and parallel low current arc faults in the time and frequency domain[A]. 53rd IEEE Holm Conference on Electrical Contacts[C]. Charleston, USA, 2010. 1-7.
[8] Lee Weijen, Sahni M, Methaprayoon K, et al. A novel approach for arcing fault detection for medium low voltage switchgear[J]. IEEE Transactions on Industry Applications, 2009, 45(4):1475-1483.
[9] 王晓远,高淼,赵玉双(Wang Xiaoyuan, Gao Miao, Zhao Yushuang).阻性负载下低压故障电弧特性分析(Characteristic analysis of low voltage arc fault in resistive load conditions)[J].电力自动化设备(Electric Power Automation Equipment),2015,35(5):106-118.
[10] 唐金城,缪希仁,张丽萍(Tang Jincheng, Miao Xiren, Zhang Liping).基于DSP的低压配电线路电弧故障检测系统(Arc fault detection on low voltage distribution line based on DSP)[J].电工电能新技术(Advanced Technology of Electrical Engineering and Energy),2015,34(2):72-75.
[11] SAE AS5692: 2004, Arc Fault Circuit Breaker(AFCB), aircraft, trip-free single phase 115 VAC, 400Hz-constant frequency[S].
[12] UL Inc. UL standard for safety for arc fault circuit interrupters UL 1699[M]. Second Edition. Northbrook: Underwriter Laboratories Inc, 2006. 16-24.
[13] 郭庆丰(Guo Qingfeng).高阶统计量在滚动轴承故障诊断中的应用(High order statistics in rolling bearing fault Diagnosis) [D].西安:西安电子科技大学(Xi’an: Xi’an University of Electronic Science and Technology),2013.
[14] 刘德军,鲍光海,孙怀平(Liu Dejun, Bao Guanghai, Sun Huaping). 基于峰值指标和脉冲指标的串联电弧故障检测研究(Research on fault detection of series arc based on peak index and pulse index)[J].电气与能效管理技术(Electrical and Energy Management Technology),2017, (16):6-10, 38.
[15] 陈绍炜,柳光峰,冶帅,等(Chen Shaowei, Liu Guangfeng, Ye Shuai, et al.). 基于蝙蝠算法优化ELM的模拟电路故障诊断研究(Research for analog circuit fault diagnosis based on ELM optimized by bat algorithm)[J].电子测量技术(Electronic Measurement Technology),2015,38(2):138-141.
[16] 张英堂,马超尹,尹刚,等(Zhang Yingtang, Ma Chaoyin, Yin Gang, et al.).基于多极限学习机在线集成的柴油机故障诊断方法研究(Diesel engine fault diagnosis based on online ensemble of multiple extreme learning machine)[J].车用发动机(Vehicle Engine),2012,(6):85-89.
[17] 夏威,张麟,袁秋实,等(Xia Wei, Zhang Lin, Yuan Qiushi, et al.).改进极限学习机应用于电网故障诊断(Application of improved extreme learning machine in power system fault diagnosis)[J].电网与清洁能源(Power System and Clean Energy), 2015,31(4):15-19.
[18] 尹刚,张英堂,李志宁,等(Yin Gang, Zhang Yingtang, Li Zhining, et al.). 自适应集成极限学习机在故障诊断中的应用(Application of adaptive integrated limit learning machine in fault diagnosis)[J].振动、测试与诊断(Journal of Vibration, Testing and Diagnosis),2013,33(5):897-901.
[19] Mirjalili S, Lewis A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61.
[20] 白媛,陈京荣,展之婵(Bai Yuan, Chen Jingrong, Zhan Zhichan).改进灰狼优化算法的研究与分析(Research and analysis of improved gray wolf optimization algorithm)[J].计算机科学与应用(Computer Science and Application),2017,7(6):562-571.