A new series arc fault identification method based on wavelet transform

Series arc fault is an important incentive for electrical fires. Wavelet transform is a widely used series arc fault identification method. However, it is difficult to distinguish the normal condition and arc fault when only using wavelet transform, and a large amount of redundant data will be generated. To solve this problem, this paper presents a new series arc fault identification method which based on wavelet transform and singular value decomposition. The experiment platform was built according to UL1699, and the method was verified by experiments. The method presented in this paper can well distinguish the series arc fault and normal state. The accuracy of the series arc fault identification is high, and the redundant data is greatly reduced.

[1]  Xiu Yao,et al.  Characteristic Study and Time-Domain Discrete- Wavelet-Transform Based Hybrid Detection of Series DC Arc Faults , 2014, IEEE Transactions on Power Electronics.

[2]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[3]  Nam Chul Kim,et al.  Comments on “SVD-Based Modeling for Image Texture Classification Using Wavelet Transform” , 2013, IEEE Transactions on Image Processing.

[4]  Zhan Wang,et al.  Evaluation method for arc fault detection algorithms , 2014, 2014 IEEE 40th Photovoltaic Specialist Conference (PVSC).

[5]  G. D. Gregory,et al.  The arc-fault circuit interrupter, an emerging product , 1998, 1998 IEEE Industrial and Commercial Power Systems Technical Conference. Conference Record. Papers Presented at the 1998 Annual Meeting (Cat. No.98CH36202).

[6]  C.E. Restrepo,et al.  Arc Fault Detection and Discrimination Methods , 2007, Electrical Contacts - 2007 Proceedings of the 53rd IEEE Holm Conference on Electrical Contacts.

[7]  Zhang Rencheng.  Study on the Feature Extraction of Fault Arc Sound Signal Based on Wavelet Analysis , 2008 .

[8]  Xuezhi Zhao,et al.  Extraction Method of Faint Fault Feature Based on Wavelet-SVD Difference Spectrum , 2012 .

[9]  Kong Deyu.  Research on Arc-Fault Detection Based on Difference-Root Mean Square Method , 2013 .

[10]  Malay Kishore Dutta,et al.  An efficient audio watermarking scheme based on SVD in wavelet domain and Chaotic Mapping , 2012, 2012 Nirma University International Conference on Engineering (NUiCONE).

[11]  Ouyang You-peng.  Partial Discharge Pattern Recognition Using Discrete Wavelet Transform and Singular Value Decomposition , 2010 .

[12]  Weiyan Zheng,et al.  Detecting low-voltage arc fault based on lifting multiwavelet , 2009, 2009 Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA).

[13]  J Prezelj,et al.  Evaluation of the sound signal based on the welding current in the gas—metal arc welding process , 2003 .

[14]  Zhengxiang Song,et al.  A method for arc fault detection based on the analysis of signal's characteristic frequency band with wavelet transform , 2013, 2013 2nd International Conference on Electric Power Equipment - Switching Technology (ICEPE-ST).

[15]  Kon B. Wong,et al.  More about arc-fault circuit interrupters , 2003 .

[16]  Xiaobin Zhang,et al.  Characteristics analysis and detection of AC arc fault in SSPC based on wavelet transform , 2016, 2016 IEEE International Conference on Aircraft Utility Systems (AUS).

[17]  Robert S. Balog,et al.  Arc Fault and Flash Signal Analysis in DC Distribution Systems Using Wavelet Transformation , 2015, IEEE Transactions on Smart Grid.

[18]  Marko V. Jankovic,et al.  The Detection of Series Arc Fault in Photovoltaic Systems Based on the Arc Current Entropy , 2016, IEEE Transactions on Power Electronics.

[19]  Randall K. Noon.  Engineering analysis of fires and explosions , 1995 .