Series arc fault detection in photovoltaic system by small‐signal impedance and noise monitoring

Abstract

Summary This paper presents a new method for the fast detection of series electrical arc (SEA) fault in a photovoltaic (PV) system, which relies on a power converter output switching waveforms as the excitation signal used to identify PV small‐signal impedance at switching frequency. In addition to the small‐signal impedance value, the proposed method extracts the high frequency noise power value caused by SEA from the PV current and uses it to indicate the presence of SEA. Both theoretical analysis and measurements performed on a commercial PV system show that SEA causes a step‐like increase in small‐signal reactance. SEA was detected in less than 10 ms during tests of the proposed algorithm. Moreover, various tests showed that there was no false tripping during transients in normal operation. Finally, the proposed algorithm was implemented and tested with a commercially available digital signal processor.

A Dynamic-SUGPDS Model for Faults Detection and Isolation of Underground Power Cable Based on Detection and Isolation Algorithm and Smart Sensors

“…In the last decade, a vast number of research work have been reported on different aspects related to various faults in the area of underground power distribution system likely analysis of faults [25][26][27][28][29][30], adaptive fault [31][32][33], SC, OC and ground faults [34][35][36][37], detection [38,39], classification [40] and fault location [41][42][43][44][45][46], sustainable protection [47,48], noise monitoring [49], and for thermal test [50].…”