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Timely and accurate detection of anomalies in power electronics is becomingincreasingly critical for maintaining complex production systems. Robust andexplainable strategies help decrease system downtime and preempt or mitigateinfrastructure cyberattacks. This work begins by explaining the types ofuncertainty present in current datasets and machine learning algorithm outputs.Three techniques for combating these uncertainties are then introduced andanalyzed. We further present two anomaly detection and classificationapproaches, namely the Matrix Profile algorithm and anomaly transformer, whichare applied in the context of a power electronic converter dataset.Specifically, the Matrix Profile algorithm is shown to be well suited as ageneralizable approach for detecting real-time anomalies in streamingtime-series data. The STUMPY python library implementation of the iterativeMatrix Profile is used for the creation of the detector. A series of customfilters is created and added to the detector to tune its sensitivity, recall,and detection accuracy. Our numerical results show that, with simple parametertuning, the detector provides high accuracy and performance in a variety offault scenarios.

A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics