Fault Detection Methods in a Power Distribution System with DER: A Review

The increasing integration of distributed energy resources (DERs) has transformed conventional radial distribution networks into active distribution systems characterized by bidirectional power flow, variable fault currents, and dynamic operating conditions. While DERs improve system reliability, efficiency, and sustainability, they introduce significant challenges to conventional protection and fault detection schemes. This paper presents a comprehensive review of fault detection methods for DER-integrated distribution networks, including Support Vector Machine (SVM) approaches, neural network-based methods, harmonic analysis techniques, and adaptive protection strategies. The impact of DER penetration on protection coordination, nuisance tripping, protection blindness, high-impedance fault (HIF) detection, and islanding is critically examined. In addition, passive, active, and artificial intelligence (AI)-based islanding detection methods are reviewed and compared in terms of detection accuracy, non-detection zone (NDZ), and detection speed. This review shows that AI-based approaches, particularly deep learning and hybrid machine learning techniques, consistently achieve superior fault and islanding detection performance. However, most reported methods are validated using simulation-generated datasets under predefined operating conditions, limiting confidence in their practical deployment. This study highlights the need for large-scale field validation, real-world datasets, hardware implementation, and robust protection frameworks capable of accommodating high DER penetration levels and evolving grid operating conditions.