Broad Learning Based Fault Detection and Diagnosis Method for Three-Phase Six Switch Converter

Since the last few decades, research on power converters has been critical of the power electronics field. In recent years, the two level converter has gained a lot of attention due to its simple structure and control for various applications. From the design phase to manufacturing, converter reliability is a key performance indicator that must be considered. Due to the vast number of interdependent components and subassemblies that constitute the 2L converter, it is vulnerable to a wide variety of failure modes and processes. Fault-tolerant ability of the topologies is very crucial for the reliable operation of the overall system. This paper provides an analytical study of the fault-tolerant ability of the 2L converter with open-circuit fault (OCF). This analysis has been used for the detection and identification of faulty components using less computational complex broad learning system. The superior fault recognition capability of the proposed work proves the robustness of the proposed method for the fault diagnosis of 2L power converter.