Artificial Intelligence for DC Arc Fault Detection in Photovoltaic Systems: A Comprehensive Review - Kamal Chandra Paul; Disnebio Waldmann; Chen Chen; Yao Wang; Tiefu Zhao (IEEE Access)
Photovoltaic (PV) systems are increasingly used for renewable energy generation but remain vulnerable to series arc faults, which can cause serious safety risks, fire hazards, and system failures. Detecting these faults in DC circuits is challenging due to their subtle electrical signatures and the presence of noise from system and environmental sources. Traditional detection methods often fall short in terms of accuracy, prompting growing interest in artificial intelligence (AI)-based solutions. This review article provides a comprehensive analysis of AI-based techniques for series arc fault detection in PV systems, covering key aspects such as data preprocessing, feature extraction, model optimization, and hardware implementation. It presents a structured comparison of existing methods, including their strengths and limitations, through descriptive discussion and summary tables. The review also includes a simplified flowchart to illustrate the typical AI-based DC arc fault detection process. Key challenges are discussed, along with future directions such as hybrid models, transfer learning, and implementation in resource-constrained edge devices. This work aims to support continued research and development by helping researchers and engineers better understand the strengths and limitations of current approaches and identify practical ways to improve arc fault detection for enhanced PV system safety and reliability.