Fault Diagnosis in Three-Phase Voltage Source Inverter Using Machine Learning Techniques

This chapter delves into the critical role of fault diagnosis in three-phase voltage source inverters (VSIs), which are essential components in industrial environments, hybrid electric vehicles, and renewable energy systems. The text explores the common failures in power converters, such as open and short circuits, and the economic and operational impacts of these faults. It presents a detailed analysis of various machine learning techniques, including artificial neural networks, support vector machines, and convolutional neural networks, for detecting and diagnosing faults. The chapter also compares different feature extraction methods, such as three-dimensional plots and direct-quadrature-zero transformation, highlighting their effectiveness in fault classification. The results demonstrate that the three-dimensional feature extraction technique offers superior accuracy and speed in identifying single, double, and triple faults. The chapter concludes with a discussion on the potential of machine learning in enhancing the reliability and performance of three-phase inverters, emphasizing the need for continuous research and development in this area.