Digital Twin-Driven Health Management in Microgrids

Abstract

The increasing shift towards decentralized energy systems have made microgrids (MGs) a key solution for improving energy reliability, resilience, and sustainability. However, managing the complexities of MG operations poses significant challenges. Traditional approaches often struggle to address these complexities, resulting in inefficiencies and compromised reliability. In response, this paper presents a digital twin (DT) framework designed for decision-making and real-time fault detection and diagnosis tested in a lab-scale DC microgrid. The DT model replicates the dynamics of the physical system (PS) under various fault conditions. An AI-driven Long Short-Term Memory (LSTM) model is integrated into the framework to improve health management. The performance of the system is assessed under both normal and emergency conditions using metrics such as Root Mean Square Error (RMSE) and confusion matrices. A comparative analysis between the DT and PS validates the DT’s ability to detect and diagnose faults accurately, demonstrating its potential to enhance microgrid operational resilience.