Intelligent strategies for microgrid protection: A comprehensive review

Nirma Peter, Pankaj Gupta and Nidhi Goel

Applied Energy, 2025, vol. 379, issue C, No S0306261924022840

Abstract: The power system is increasingly integrating more and more renewable energy sources to meet rising power demand and address environmental concerns related to greenhouse gas emissions. However, these sources are in the form of local energy and are embedded into the local low-voltage distribution grid through inverters. The development of microgrids transforms the power system into a bidirectional infrastructure, enabling the distribution grid to both import and export power within the distribution grid. This affects the performance of conventional overcurrent protection, which traditionally manages unidirectional current flow. The integration of these sources presents several protection challenges, including variations in short-circuit currents under different operating conditions, limitations in conventional protection methods, and the need for effective relay coordination. These challenges led to the emergence of intelligent protection strategies capable of processing and analyzing large volumes of data, facilitating real-time decision-making and accurate fault detection. A bibliometric study analyzes research trends in intelligent protection strategies for microgrids. This study reviews various intelligent protection schemes implemented in AC, DC, and AC/DC hybrid microgrids, categorizing them based on their decision-making modules, outlining their limitations, and emphasizing potential solutions. It provides insights into the protective features, performance evaluation, and applicability of these intelligent methods across different microgrid types. Limited literature is available that specifically reviews various intelligent protection strategies for microgrids. This paper provides insights into the transformative role of intelligent technologies in microgrid protection.

Keywords: Intelligent protection system; Distributed generation; Fault detection; Microgrid; Machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.124901

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