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All Journal Control Systems and Optimization Letters Signal and Image Processing Letters
Islam, Md Shoriful
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Journal : Control Systems and Optimization Letters

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A Comprehensive Review of AI-Driven DC Arc Fault Detection in Photovoltaic Systems Islam, Md Shoriful
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.208

Abstract

Photovoltaic (PV) systems now account for over 1.3% of global electricity generation and are expanding rapidly. DC arc faults pose severe safety risks among PV system faults, including fire hazards, equipment damage, and system failures. Traditional protection methods such as overcurrent devices and threshold-based detection often fail to distinguish arc faults from normal system noise reliably due to series arcs' intermittent and low-current nature. To address these problems, intelligent methods, including machine learning (ML), deep learning (DL), and hybrid approaches, have emerged as promising solutions offering superior accuracy, adaptability, and real-time performance. This paper presents a state-of-the-art intelligent approach for DC arc fault detection in PV systems. We explicitly compare ML and DL algorithms, highlighting trade-offs in computational efficiency, data requirements, and hardware constraints. Key implementation challenges, limited real-world datasets, and high computational costs for edge deployment are analyzed. Future directions focus on bridging gaps such as edge computing for real-time detection, synthetic data generation, and interpretable AI models. The findings aim to advance PV safety standards and enable scalable renewable energy integration.
DC Arc Fault Detection in Microgrids: A Comprehensive Review of Challenges, Advances, and Future Directions Islam, Md Shoriful
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.244

Abstract

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.
Co-Authors Elme, Nafisa Sultana Halim, Md Abdul Hossain, Md Momin Khan, Md Yakub Ali