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.

开关故障的定位和检测是提高逆变器可靠性的关键步骤，这是关键应用的要求。本文重点介绍一种基于机器学习的方法，用于中高功率应用中使用的级联 H 桥 (CHB) 多电平逆变器 (MLI) 的开路开关故障检测系统。在此分析中考虑了每个开关故障，并使用该问题对逻辑上不同的机器学习算法进行建模，即高斯朴素贝叶斯 (GNB)、支持向量机 (SVM) 和多项逻辑回归 (MLR) 模型，用于故障识别和分析基于多类分类（MCC）方法的定位。这项工作包括从五级 CHB-MLI 进行模拟和基于硬件的数据收集，以及最终数据集中基于相关性的突出特征选择。针对所有可能的预测器（特征）组合的三个 MCC 模型的结果证明，桥的平均电压是 CHB 逆变器中故障检测的主要特征。其他主要发现是机器学习辅助的故障识别和诊断无疑是准确的，并且在 CHB-MLI 的状态监测和故障诊断中更有前景。结果显示故障诊断准确率100%。因此，本文的工作证实了 CHB-MLI 中的开关故障检测以平均值作为主要故障特征以及机器学习技术简单、鲁棒且准确的故障检测。