Enhancing Open Circuit Switch Fault Localization in Two-Level Voltage Source Inverters through Machine Learning with 3D Current Trajectory Analysis

This paper proposes an enhanced approach for localizing open circuit faults (OCFs) in two-level voltage source inverters (VSIs). OCFs in VSIs can lead to critical system failures which can also be catastrophic if not localized or attended to within a small time-frame. While detecting the OCFs is important, accurate localization of OCFs is paramount as it helps in reducing the downtime of the system. Leveraging machine learning algorithms, including a neural-based classifier called the Wide Neural Network (NN) and a non-neural classifier known as the Subspace KNN, the proposed study demonstrates promising results. The Wide NN achieves an accuracy of 97.16%, while the Subspace KNN achieves an impressive accuracy of 99.66% in fault localization. These high accuracies contribute to improved VSI reliability in various industrial applications, advancing the field of power electronics by exploring innovative fault detection and localization techniques.