Enhancing Microgrid Resilience with LSTM and Fuzzy Logic for Predictive Maintenance

Abstract

Microgrids have become integral to modern energy systems, providing decentralized and resilient energy solutions. However, ensuring the reliability of microgrid assets poses significant challenges, particularly given aging infrastructure and unpredictable environmental conditions. While existing methods—such as predictive maintenance, real-time monitoring, and fault detection utilizing Support Vector Machines (SVM), Random Forests, and Principal Component Analysis (PCA)—enhance reliability, they often fall short due to insufficient multidimensional data analysis and limited support for realistic decision-making. This underscores the need for advanced approaches in microgrid management. In this paper, we propose an innovative machine learning-based methodology that integrates Long Short-Term Memory (LSTM) networks with fuzzy logic for predictive maintenance of microgrid assets. The proposed approach effectively addresses the inherent fluctuations and dynamic behavior of microgrids, enhancing system resilience and reducing downtime. By leveraging LSTM's ability to capture temporal patterns alongside fuzzy logic's capacity for handling uncertainties, the method proactively identifies and mitigates potential equipment failures. Traditional maintenance strategies predominantly rely on reactive mechanisms, resulting in higher costs and increased system vulnerabilities. Simulation results indicate that the proposed algorithm achieves a 10% to 40% improvement in fault detection across varying failure levels, demonstrating significant advantages over conventional techniques.