Research on Low-voltage Series Arc Fault Detection Method Based on Least Squares Support Vector Machine

Arc fault is one of the important reasons of electrical fires. In virtue of cross talk, randomness and weakness of series arc faults in low-voltage circuits, very few of techniques have been well used to protect loads from series arc faults. Thus, a novel detection method based on support vector machine is developed in this paper. If series arc fault occurs, high frequency signal energy in circuit will increase a lot, and current cycle integrals are variable and erratic. However, high frequency signal energy will be influenced by cross talk in a nearby branch circuit. Besides, current cycle integrals will al- so vary while the working states of circuit changed. To better describe series arc faults, two characteristics include high frequency signal energy and current integral difference are extracted as support vectors. Based on these support vectors, least squares support vector machine is used to distinguish series arc faults from normal working states. The validity of the developed method is verified via an arc fault experimental platform set up. The results show that series arc faults are well detected based on the developed method.

[1]  Rencheng Zhang,et al.  Study on In-Process Detection and Diagnosis of Faults Arc Based on Early Sounds Signature and Intermittent Chaos , 2008 .

[2]  D. Stade,et al.  Arc fault model for low-voltage AC systems , 2005, IEEE Transactions on Power Delivery.

[3]  S. Weber,et al.  Modeling of a Domestic Electrical Installation to Arc Fault Detection , 2012, 2012 IEEE 58th Holm Conference on Electrical Contacts (Holm).

[4]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[5]  Charles Kim.  Electromagnetic Radiation Behavior of Low-Voltage Arcing Fault , 2009 .

[6]  Fang Huai-ying.  Early detecting of fault arcs using Lyapunov exponents , 2008 .

[7]  Deng Ju.  Compound Electric Field Prediction Method for the UHVDC Transmission Lines Based on Fuzzy Clustering and Least Squares Support Vector Machine , 2013 .

[8]  Qiang Cheng,et al.  Confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine , 2014, Pattern Recognit. Lett..

[9]  Jing Yang,et al.  An Improved Random Forest Algorithm for Class-Imbalanced Data Classification and its Application in PAD Risk Factors Analysis , 2013 .

[10]  L. Martirano,et al.  Simplified arc-fault model: The reduction factor of the arc current , 2012, 2012 IEEE Industry Applications Society Annual Meeting.

[11]  T. Gammon,et al.  Arcing-fault models for low-voltage power systems , 2000, 2000 IEEE Industrial and Commercial Power Systems Technical Conference. Conference Record (Cat. No.00CH37053).

[12]  Patrick Schweitzer,et al.  Method to Design Arc Fault Detection Algorithm Using FPGA , 2011, 2011 IEEE 57th Holm Conference on Electrical Contacts (Holm).

[13]  Rencheng Zhang,et al.  Fuzzy Clustering Algorithm of Early Fire Based on Process Characteristic , 2010 .

[14]  Massimiliano Pontil,et al.  Properties of Support Vector Machines , 1998, Neural Computation.

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

[16]  Xiaowei Yang,et al.  A robust least squares support vector machine for regression and classification with noise , 2014, Neurocomputing.