  Timely and accurate detection of anomalies in power electronics is becoming
increasingly critical for maintaining complex production systems. Robust and
explainable strategies help decrease system downtime and preempt or mitigate
infrastructure cyberattacks. This work begins by explaining the types of
uncertainty present in current datasets and machine learning algorithm outputs.
Three techniques for combating these uncertainties are then introduced and
analyzed. We further present two anomaly detection and classification
approaches, namely the Matrix Profile algorithm and anomaly transformer, which
are applied in the context of a power electronic converter dataset.
Specifically, the Matrix Profile algorithm is shown to be well suited as a
generalizable approach for detecting real-time anomalies in streaming
time-series data. The STUMPY python library implementation of the iterative
Matrix Profile is used for the creation of the detector. A series of custom
filters is created and added to the detector to tune its sensitivity, recall,
and detection accuracy. Our numerical results show that, with simple parameter
tuning, the detector provides high accuracy and performance in a variety of
fault scenarios.
