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Series Arc Fault Detection Algorithm Based on Autoregressive Bispectrum Analysis

1
College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
2
School of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou 350118, China
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Author to whom correspondence should be addressed.
Academic Editor: Javier Del Ser Lorente
Algorithms 2015, 8(4), 929-950; https://0-doi-org.brum.beds.ac.uk/10.3390/a8040929
Received: 3 July 2015 / Revised: 24 September 2015 / Accepted: 28 September 2015 / Published: 16 October 2015
Arc fault is one of the most critical reasons for electrical fires. Due to the diversity, randomness and concealment of arc faults in low-voltage circuits, it is difficult for general methods to protect all loads from series arc faults. From the analysis of many series arc faults, a large number of high frequency signals generated in circuits are found. These signals are easily affected by Gaussian noise which is difficult to be eliminated as a result of frequency aliasing. Thus, a novel detection algorithm is developed to accurately detect series arc faults in this paper. Initially, an autoregressive model of the mixed high frequency signals is modelled. Then, autoregressive bispectrum analysis is introduced to analyze common series arc fault features. The phase information of arc fault signal is preserved using this method. The influence of Gaussian noise is restrained effectively. Afterwards, several features including characteristic frequency, fluctuation of phase angles, diffused distribution and incremental numbers of bispectrum peaks are extracted for recognizing arc faults. Finally, least squares support vector machine is used to accurately identify series arc faults from the load states based on these frequency features of bispectrum. The validity of the algorithm is experimentally verified obtaining arc fault detection rate above 97%. View Full-Text
Keywords: arc fault detection; autoregressive model; bispectrum analysis; Gaussian noise; high frequency signal; electrical fire arc fault detection; autoregressive model; bispectrum analysis; Gaussian noise; high frequency signal; electrical fire
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MDPI and ACS Style

Yang, K.; Zhang, R.; Chen, S.; Zhang, F.; Yang, J.; Zhang, X. Series Arc Fault Detection Algorithm Based on Autoregressive Bispectrum Analysis. Algorithms 2015, 8, 929-950. https://0-doi-org.brum.beds.ac.uk/10.3390/a8040929

AMA Style

Yang K, Zhang R, Chen S, Zhang F, Yang J, Zhang X. Series Arc Fault Detection Algorithm Based on Autoregressive Bispectrum Analysis. Algorithms. 2015; 8(4):929-950. https://0-doi-org.brum.beds.ac.uk/10.3390/a8040929

Chicago/Turabian Style

Yang, Kai; Zhang, Rencheng; Chen, Shouhong; Zhang, Fujiang; Yang, Jianhong; Zhang, Xingbin. 2015. "Series Arc Fault Detection Algorithm Based on Autoregressive Bispectrum Analysis" Algorithms 8, no. 4: 929-950. https://0-doi-org.brum.beds.ac.uk/10.3390/a8040929

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