Arc Fault Detection in Photovoltaic DC Power Systems: A Comprehensive Review of Arc Modeling, Signal Processing, and AI-Based Techniques

The rapid growth of renewable energy, especially solar photovoltaic (PV) power, is expanding worldwide and supporting the main load in DC power systems and microgrid ( μ G) networks. However, the long-term performance of PV systems can degrade due to their intermittent nature, direct exposure to weather conditions, high-voltage operations, aging wires, and improper connections, all of which increase the risk of arc faults (AFs). These faults pose serious safety risks, including fire hazards, equipment failure, and even threats to human lives. Detecting PV-based DC AFs remains challenging due to the varying electrical and physical behavior of arcs and the absence of natural current zero-crossing in the DC system. This review article presents a comprehensive analysis of AF detection methods, covering traditional and advanced signal processing and multidomain feature engineering techniques as well as state-of-the-art artificial intelligence (AI)-based approaches for DC series AF detection, particularly in PV systems. Various detection schemes are compared and their limitations are critically discussed. Additionally, this article reviews DC series AF detection under uncertain real-world conditions, mitigation and limitation strategies, and industry-related case studies. Furthermore, it emphasizes the significance of conventional empirical, physical-based AFs modeling to advanced and adaptive multiphysics arc model, and simulation studies, highlighting their essential role in developing accurate and reliable detection algorithms.