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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Electrical Power & Energy Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
