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• W2340372786 startingPage "149" @default.
• W2340372786 abstract "Dangerous serial electric arc faults on low voltage power lines must be detected before fire hazards occur. The detection technology is requested to have high accuracy. However, the characteristics of line current waveform during serial arc faults are complicated. This paper uses the approach of a radial basis function neural network (DRBFNN) to identify the occurrence of serial arc faults. At first, the discrete wavelet transform (DWT) is employed to obtain the time–frequency domain characteristics of line current waveforms to reflect the serial arc fault patterns. Then some measured data are used to train the DRBFNN. Finally, this study compares the detection results under different loading conditions and operation conditions. It also compares the detection results with other two methods, detection of sub-spectrum energy (DSE) and high frequency detection by wavelet transform (HFDWT). It can be observed that DRBFNN has better ability than DSE and HFDWT to detect serial arc faults." @default.
• W2340372786 created "2016-06-24" @default.
• W2340372786 creator A5027916759 @default.
• W2340372786 creator A5061180661 @default.
• W2340372786 creator A5063637510 @default.
• W2340372786 date "2016-12-01" @default.
• W2340372786 modified "2023-09-25" @default.
• W2340372786 title "Detection of serial arc fault on low-voltage indoor power lines by using radial basis function neural network" @default.
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• W2340372786 doi "https://doi.org/10.1016/j.ijepes.2016.04.013" @default.
• W2340372786 hasPublicationYear "2016" @default.
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