<![CDATA[DC arc faults in residential, commercial, and industrial DC microgrids pose significant safety and reliability challenges, including potential fire hazards, equipment damage, and system downtime. Despite advancements in detection technologies, accurately detecting and mitigating DC arc faults remains difficult due to the dynamic nature of microgrids, fluctuating load conditions, and the absence of zero-crossing points in DC systems. This review provides a thorough analysis of existing DC arc-fault detection methods, including time-domain, frequency-domain, time-frequency analysis, and machine learning techniques, and compares their performance in terms of accuracy, robustness, and real-time applicability. The review highlights the principles, advantages, and limitations of each approach, addressing key challenges such as noise interference, low-current arc detection, and the need for real-time processing. Furthermore, it discusses recent developments in hybrid detection systems, high-frequency signal processing, and deep learning models as promising solutions to enhance detection accuracy and system reliability, while also addressing practical implementation challenges. Finally, the review outlines future research directions, emphasizing the importance of adaptive algorithms, standardized testing protocols, and integration with emerging grid technologies. This review distinguishes itself by providing a systematic comparison of detection paradigms and a synthesized roadmap for future research, bridging the gap between theoretical advances and practical implementation in diverse microgrid environments.]]>
Copyrights © 2025
