FMEA 2.0: Machine Learning Applications in Smart Microgrid Risk Assessment

Modern Smart Grids are complex systems incorporating physical components like distributed energy resources and storage, along with cyber components for advanced control, networking, and monitoring. Reliability assessment is a systematic evaluation process used to identify, interpret, and eliminate potential failures and enhance overall reliability of the systems such as Smart Grids where safety is a critical factor. Failure Mode and Effects Analysis (FMEA) is one of the risk analysis tools used for evaluating and prioritizing potential failure modes and improving reliability of systems and processes. Within traditional FMEA, failure modes are ranked using a numerical assessment called the Risk Priority Number (RPN), derived from the multiplication of three factors: severity, occurrence, and detection. While commonly used, the FMEA approach has shortcomings, such as assuming equal importance of factors and not adhering to the ordered weighted rule. For this reason, this study proposes an integrated methodology for risk prioritization and failure mode classification into low, moderate and high-risk faults using Grey Relational Analysis (GRA) together with FMEA and Deep Learning algorithms. The results demonstrate that, especially in complex systems like Smart Microgrids, the proposed method more accurately captures the coupling relationships between failure modes compared to the conventional FMEA method.