Toward High-Fidelity and Trustworthy Digital Twins for Fault Diagnosis in Grid Connected Inverters

Automating the task of fault detection and diagnosis is essential for reducing the operational and maintenance costs of power electronic converters. Hence, this paper introduces an online optimization methodology for digital twins to diagnose multiple faults in grid connected inverters. In addition to enhancing accuracy, we channelize our efforts towards reducing computational complexity in parameter configuration under a limited set of training data. To address cybersecurity vulnerabilities during the training phase of our digital twin, we perform <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data sanitization</i> with the aid of a quantitative association rule mining technique. For classification performance assessment, various fault cases in a virtual synchronous generator are considered to demonstrate the efficacy of our approach. Our research outcomes reveal increased accuracy and fidelity levels achieved by our digital-twin design, paving the way for reliable and trustworthy decision-making under anomalous conditions.