Regular Article

Advanced algorithm for fault detection and localization in DC microgrids utilizing capacitor current transient analysis for enhanced reliability in DER-integrated systems

Department of Electrical Engineering, Maulana Azad National Institute of Technology, Bhopal 462003, MP, India

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Received: 16 April 2025
Accepted: 10 February 2026

Abstract

This paper presents an advanced Short-Circuit (SC) fault detection and location methodology for DC Microgrids (DCMGs) integrating Distributed Energy Resources (DERs). Unisolated SC faults in DC systems present a significant challenge, leading to service disruptions and hindering effective fault detection. To address this, the proposed method capitalizes on the capacitor-dominated characteristics of DCMGs by utilizing capacitor current dynamics. A comprehensive DCMG system model is developed to facilitate the application and evaluation of the proposed scheme. The algorithm employs the average capacitor current and cable resistance to accurately determine the occurrence and location of faults within the DCMG network. Active DER sources (solar PV, wind, utility grid, and batteries) connect to loads via DC–DC converters, cables, relays, and circuit breakers. The method effectively detects the average capacitor current, enabling the identification of internal faults. Faults are classified as external if a set criterion is not met. Furthermore, the paper proposes a redundancy-based isolation configuration for zonal-type distributed networks. The efficacy of the methodology is rigorously validated through digital simulation studies. MATLAB/Simulink simulations of a DCMG, incorporating diverse generation sources and loads, are conducted under various fault scenarios. These include internal and external faults, as well as Line-to-Ground (LG) and Line-to-Line (LL) faults. The simulation results demonstrate the method’s ability to accurately detect both Low-Impedance Faults (LIFs) and High-Impedance Faults (HIFs) and to locate the faulty cable. Notably, the approach achieves fault cable detection and isolation within 2.3 ms, confirming its effectiveness and speed.