Fault prediction of power electronics modules and systems under complex working conditions

Power electronics modules such as inverters and rectifiers are crucial in industry and they are indispensable in various power conversion systems. There have been many studies on the fault diagnosis of power converters or power modules in the system but recently more attention has been paid on predicting failures. Most conventional techniques often rely on accurate physical models or high frequently sampled electric signals in simulation or experiment environments. In practice, however, the life and the degradation of power electrics devices are highly influenced by loads and operation regimes. The analysis needs considering various identical or similar devices in a networked power grid as well. Thus, it is not trivial to achieve the predictive analysis in such complex working conditions. This paper presents a systematic approach investigating the fault prediction of power converters in power conversion systems. Two data-driven methods with novel techniques, which take into account working condition variances and the data imbalance, have been developed and applied to an industry use case where only high level system heartbeat signals are available. These methods are validated to effectively predict the power converter failures.