Fault detection design for three-phase voltage source inverter in power train applications

Abstract

This thesis is dedicated to an extensive exploration of fault detection methods tailored specifically for three-phase inverters, critical components within the propulsion systems of Electric Vehicles (EVs) and drive systems in general. A notable focal point of this study involves the application of advanced signal processing techniques to adeptly identify and diagnose potential faults. Through the signal processing mixed clustering technique, we are able to use the proposed algorithm to compare the reference gate-driving signal with the actual output voltage of the voltage source inverter (VSI) to detect the occurrences of faults. Furthermore, to facilitate this investigative journey, intricate simulation models are thoughtfully crafted utilizing the PSIM software platform. These models not only serve as practical testbeds for the proposed fault detection methods but also enable a thorough analysis and assessment of their efficacy. Some preliminary experimental results are also included to provide proof of principle.