Machine Learning Based Open Switch Fault Detection and Localization of Inverters

Abstract

The location and detection of switch faults is a crucial step in improving the dependability of inverters, which is a requirement for critical applications. This paper focuses on a machine learning-based approach for the open-switch fault detection system for cascaded H-bridge (CHB) multilevel inverters (MLI) used in high- and medium-power applications. Each switch failure is taken into account in this analysis, and the problem is used to model logically different machine learning algorithms, namely Gaussian Naive Bayes (GNB), support vector machines (SVM) and multinomial logistic regression (MLR) models for fault identification and localization based on multi-class classification (MCC) methods. This work includes the simulation and hardware-based data collection from five-level CHB-MLI and correlation-based selection of prominent features in the final dataset. The results of three MCC models for all possible combinations of predictors (features) prove that the mean voltage of bridges is the dominant feature for fault detection in the CHB inverter. Other major findings are machine learning-assisted fault identification and diagnosis is undoubtedly accurate and significantly more promising in condition monitoring and fault diagnosis in CHB-MLIs. The results reveal 100% accuracy in fault diagnosis. Therefore, the presented work confirms switch fault detection in CHB-MLI with mean value as dominant fault feature along with machine learning techniques simple, robust and accurate fault detection.