Series Arc Fault Detection in Low Voltage Distribution System with Signal Processing and Machine Learning Approach

Abstract

Electrical problems such as aging of electrical conductors and loose connections lead to arc fault in the electrical systems. Arcing temperature approaches up to 1000 °C which melts the conductors and leads to electrical fire accidents. Due to arcing, the current in the path gets reduced and thus conventional protection circuit may not be triggered. Thus, there is a need for designing alternate low-cost reliable arc detectors to detect and report the electrical arcing. Therefore, a series arc detection system is necessary to the low voltage distribution system (LVDS) for the reliable and efficient operation of the LVDS. In this paper, empirical mode decomposition (EMD) and support vector machine (SVM) based series arc detection in LVDS has been proposed. The arc faults have been generated in the lab with an experimental setup. When arc faults are generated, the conducted electromagnetic radiation (EMR) is present in the cable. The conducted EMR as an arc fault signature is acquired with the current transformer (CT) and digital storage oscilloscope (DSO). The arc fault signature has been extracted by the EMD analysis and, with the help of the SVM classifier, the arc fault has been detected.