基于磁通不对称分布的串联电弧故障检测研究
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摘要
目前串联电弧故障检测以主线路电流为判断对象,该方法容易受非线性负载正常工作电流奇异性的影响而造成误判;而且小功率支路的电弧故障特征容易被干路电流"淹没"而造成漏判。为了解决该问题,根据电弧高频电流电磁耦合机理,提出一种基于L-N线不对称分布和高阶累积量识别的检测方法。通过分析电弧熄灭重燃时高频剩余磁通的耦合信号,利用高阶统计量工具计算出耦合信号的峭度值。对单一负载电弧故障、综合负载干路电弧故障和综合负载支路电弧故障等不同情况进行判断,得出统一峭度阈值。结果表明,该方法可有效检测和识别串联电弧故障。
Recently, the series arc fault detection uses the main line current as decision object, which is easy to be affected by the singularity of normal working current in nonlinear load and results in misjudgment. And the arc fault characteristic in the small power branch is easily ‘submerged' by the main circuit current, which leads to missed judgment. In order to solve this problem, according to the electromagnetic coupling mechanism of high frequency arc current, a detection method based on asymmetrical distribution of L-N lines and high-order cumulant identification is presented in this paper. Through analyzing the coupling signal of high frequency residual flux during the process of arcing extinction and reignition, the kurtosis of the coupling signal is calculated by means of the high-order cumulant statistical tool. In this paper, the arc faults in different conditions such as combined load main circuit arc fault and combined load branch circuit arc fault are analyzed and judged, and the unified kurtosis threshold is obtained. The results show that the method can be effectively used to detect and identify series arc faults.
Recently, the series arc fault detection uses the main line current as decision object, which is easy to be affected by the singularity of normal working current in nonlinear load and results in misjudgment. And the arc fault characteristic in the small power branch is easily ‘submerged' by the main circuit current, which leads to missed judgment. In order to solve this problem, according to the electromagnetic coupling mechanism of high frequency arc current, a detection method based on asymmetrical distribution of L-N lines and high-order cumulant identification is presented in this paper. Through analyzing the coupling signal of high frequency residual flux during the process of arcing extinction and reignition, the kurtosis of the coupling signal is calculated by means of the high-order cumulant statistical tool. In this paper, the arc faults in different conditions such as combined load main circuit arc fault and combined load branch circuit arc fault are analyzed and judged, and the unified kurtosis threshold is obtained. The results show that the method can be effectively used to detect and identify series arc faults.
引文
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[14] 曲娜,王建辉,刘金海,等.基于Cassie模型和L_(3/4)范数的串联电弧故障检测方法[J].电网技术,2018,42(12):3992-3997.QU N,WANG J H,LIU J H,et al.Series arc fault detection method based on Cassie model and L_(3/4) norm[J].Power System Technology,2018,42(12):3992-3997.
[15] 刘德军,鲍光海,孙怀平.基于峰值指标和脉冲指标的串联电弧故障检测研究[J].电器与能效管理技术,2017(16):6-10,38.LIU D J,BAO G H,SUN H P.Series arc fault detection based on indicators on peak value and pulses[J].Electrical & Energy Management Technology,2017(16):6-10,38.
[16] 黄凯,鲍光海,刘德军.基于电磁辐射的交直流电弧故障检测研究[J].电器与能效管理技术,2018(14):1- 6,12.HUANG K,BAO G H,LIU D J.AC and DC arc fault detection based on electromagnetic radiation.Electrical & Energy Management Technology,2018(14):1- 6,12.
[17] KIM C J.Electromagnetic radiation behavior of low-voltage arcing fault[J].IEEE Transactions on Power Delivery,2009,24(1):416- 423.
[18] 陈旭阳,韩振南,王志坚.改进双树复小波变换和高阶累积量的齿轮箱信号降噪方法[J].太原理工大学学报,2018,49(6):813- 819.CHEN X Y,HAN ZH N,WANG ZH J.Gearbox signal denoising method based on improved double tree complex wavelet transform and higher order cumulant[J].Journal of Taiyuan University of Technology (Social Science Edition),2018,49(6):813- 819.
[2] 郭凤仪,BILGUUN B,任雪,等.串联型故障电弧检测方法对比分析[J].工矿自动化,2017,43(8):31-36.GUO F Y,BILGUUN B,REN X,et al.Comparative analysis of series fault arc detection methods[J].Proceedings of the CSU-EPSA,2017,43(8):31-36.
[3] PULKKINEN J P.Commercial arc fault detection devices in military electromagnetic environment [J].IEEE Electromagnetic Compatibility Magazine,2018,7(4):49- 52.
[4] 张冠英,李长伟,赵远,等.基于周期均值变化率的直流故障电弧检测[J].电气工程学报,2016,11(9):44- 47,54.ZHANG G Y,LI CH W,ZHAO Y,et al.Series DC arcing fault detection method based on rate of cycle mean[J].Journal of Electrical Engineering,2016,11(9):44- 47,54.
[5] 秦雪,刘亚东,孙鹏,等.基于故障波形时频特政征配网故障识别方法研究[J].仪器仪表学报,2017,38(1):41- 49.QIN X,LIU Y D,SUN P,et al.Research on distribution network fault recognition method based on time-frequency characteristics of fault waveforms [J].Chinese Journal of Scientific Instrument,2017,38(1):41- 49.
[6] LEZAMA J,SCHWEITER P,TISSERAND E,et al.An embedded system for AC series arc detection by inter-period correlations of current[J].Electric Power Systems Research,2015(129):227-234.
[7] TISSERAND E,LEZAMA J,SCHWEITZER P,et al.Series arcing detection by algebraic derivative of the current[J].Electric Power Systems Research,2015(119):91-99.
[8] 刘艳丽,郭凤仪,王智勇,等.基于信息熵的串联型故障电弧频谱特征研究[J].电工技术学报,2015,30 (12):488- 495.LIU Y L,GUO F Y,WANG ZH Y,et al.Research on the spectral characteristics of series arc fault based on information entropy[J].Transactions of China Electrotechnical Society,2015,30(12):488- 495.
[9] 邹云峰,吴为麟,李智勇.基于自组织映射神经网络的低压故障电弧聚类分析[J].仪器仪表学报,2010,31 (3):571- 576.ZHOU Y F,WU W L,LI ZH Y.Low voltage arc fault cluster analysis based on self-organizing map[J].Chinese Journal of Scientific Instrument,2010,31(3):571- 576.
[10] JAMAL S,GHAFFARZADEH N.A new method for arcing fault location using discrete wavelet transform and wavelet networks[J].European Transactions on Electrical Power,2012,22(5):601- 615.
[11] BAQUI I,ZAMORA I,MAZONA J,et al.High impedance fault detection methodology using wavelet transform and artificial neural networks[J].Electric Power Systems Research,2011,81(7):1325-1333.
[12] 马少华,鲍洁秋,蔡志远,等.基于信息维数和零休时间的电弧故障识别方法[J].中国电机工程学报,2016,36(9):2572- 2580.MA SH H,BAO J Q,CAI ZH Y,et al.A novel arc fault identification method based on information dimension and current zero[J].Proceedings of the CSEE,2016,36(9):2572- 2580.
[13] ARTALE G,CATALIOTTI A,COSENTINO V,et al.Arc fault detection method based on CZT low-frequency harmonic current analysis[J].IEEE Transactions on Instrumentation and Measurement,2017,66(5):888- 896.
[14] 曲娜,王建辉,刘金海,等.基于Cassie模型和L_(3/4)范数的串联电弧故障检测方法[J].电网技术,2018,42(12):3992-3997.QU N,WANG J H,LIU J H,et al.Series arc fault detection method based on Cassie model and L_(3/4) norm[J].Power System Technology,2018,42(12):3992-3997.
[15] 刘德军,鲍光海,孙怀平.基于峰值指标和脉冲指标的串联电弧故障检测研究[J].电器与能效管理技术,2017(16):6-10,38.LIU D J,BAO G H,SUN H P.Series arc fault detection based on indicators on peak value and pulses[J].Electrical & Energy Management Technology,2017(16):6-10,38.
[16] 黄凯,鲍光海,刘德军.基于电磁辐射的交直流电弧故障检测研究[J].电器与能效管理技术,2018(14):1- 6,12.HUANG K,BAO G H,LIU D J.AC and DC arc fault detection based on electromagnetic radiation.Electrical & Energy Management Technology,2018(14):1- 6,12.
[17] KIM C J.Electromagnetic radiation behavior of low-voltage arcing fault[J].IEEE Transactions on Power Delivery,2009,24(1):416- 423.
[18] 陈旭阳,韩振南,王志坚.改进双树复小波变换和高阶累积量的齿轮箱信号降噪方法[J].太原理工大学学报,2018,49(6):813- 819.CHEN X Y,HAN ZH N,WANG ZH J.Gearbox signal denoising method based on improved double tree complex wavelet transform and higher order cumulant[J].Journal of Taiyuan University of Technology (Social Science Edition),2018,49(6):813- 819.