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

TL;DR

This paper presents a robust, explainable, data-driven anomaly detection method for power electronics, combining Matrix Profile and anomaly transformer techniques, with demonstrated high accuracy in real-time fault detection.

Contribution

It introduces a novel combination of Matrix Profile and anomaly transformer approaches tailored for power electronics, addressing uncertainties and enhancing detection robustness.

Findings

Matrix Profile effectively detects real-time anomalies in streaming data.

Custom filters improve detection sensitivity and accuracy.

High performance across various fault scenarios.

Abstract

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.