Abstract
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%.
Highlights
Statistical data from fire services show that over 90% of electrical fires are caused by arc faults, over currents, short circuits and leakages [1,2,3,4]
To fit with the arc fault scenarios described in UL1699 which was a standard for arc fault circuit interrupter (AFCI), a meta-model based on Mayr and Ayrton models was built [9]
AR Bispectrum analysis has been applied in many signal processing cases, but not previously in the analysis of arc faults in low-voltage alternating current (AC) circuits
Summary
Statistical data from fire services show that over 90% of electrical fires are caused by arc faults, over currents, short circuits and leakages [1,2,3,4]. The spectrum energy variations of line currents can be used to detect series arc faults [16]. Arc fault features were found by advanced signal processing approaches, and arc faults were discriminated from the load states based on these features. In order to improve the accuracy of arc fault detection, a large number of arc fault high frequency signals will be collected by a transducer to find any common features among arc faults. In order to improve the signal-to-noise ratio (SNR) and the accuracy of arc fault detection, the higher-order spectrum will be introduced in this paper.
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