Low-Voltage AC Series Arc Fault Detection Method Based on Similarity Measure Between Differential Arc Voltage and Square Wave

The types of loads greatly affect the current waveform of a series arc fault, making it difficult to construct a universal criterion for fault detection in terms of current. In contrast, arc voltage is less affected by the types of loads. So, it is easier to establish a uniform fault detection criterion by identifying arc voltage. Firstly, based on the analysis of the characteristics of differential arc voltage and arc voltage at upstream and downstream monitoring points, a fault detection method is proposed based on the similarity between the differential arc voltage and square wave at double-ended monitoring points. The method can effectively recognize drop in line voltage and fault in arc voltage, and exclude the effects of fluctuations in load. Secondly, a differential voltage capturing method based on phase information is proposed. The method uses the voltage distortion rate crossing limit at the downstream monitoring point as the starting condition of detection. The fundamental frequency phase of voltage cycle is synchronized at the double-ended monitoring point to eliminate the phase error. Nextly, the arc eigenvalues are obtained from the similarity of the differential arc voltage and square wave, and the detection strategy is determined. Finally, a novel detection solution leveraging electric power Internet of Things (IoT) technology is proposed. This study serves as a preliminary investigation, with its feasibility rigorously validated through comprehensive simulations and controlled laboratory experiments.