Key points are not available for this paper at this time.
Abstract Ensuring power system safety involves effective arc fault detection and localization. Existing devices struggle to differentiate normal and abnormal conditions, especially in confined spaces, posing precision challenges. Placing antennas strategically around the arc aids in detecting electromagnetic radiation, even in limited areas. This enables valuable data collection for real-time monitoring. To address these challenges, this paper proposes integrating experimental work using a compact multi-square microstrip antenna and signal processing techniques. The study compares the effectiveness of three signal processing approaches: threshold, Discrete Wavelet Transform (DWT), and Continuous Wavelet Transform (CWT). The signal processing technique separates genuine arc signals from background noise by identifying unique characteristics and isolating the dominant frequency. The Time of Arrival (ToA) is measured and used in Least Square (LS) and Gauss-Jordan Elimination (GJE) methods to calculate the arc source location. The outcomes illustrate the precision of the proposed model in detecting and pinpointing arc source signals, with error margins ranging from 6.15% to 7.13% for the CWT technique, 6.88% to 7.89% for the DWT technique, and 7.99% to 8.44% for the threshold technique. These results hold promise for enhancing the integration of experimental approaches in assessing arcing conditions, thereby addressing challenges in insulation systems.
Amirruddin et al. (Fri,) studied this question.