A Component Level Digital Twin Model for Power Converter Health Monitoring

The proliferation of Power Electronic Converters (PECs) has had a pervasive affect in a variety of industries including the power generation, automotive and aerospace sectors, where their use brings reliability to the forefront, especially in applications where safety critical and harsh environments are experienced. Continuous improvements in the power density and efficiency through extensive research into new semiconductor technologies, passive components, circuit topologies and control methodologies has seen the performance of PECs improve indubitably. However, the manifestation of stresses occurring from significant heating due to high currents and switching frequencies; which over time can cause degradation in the performance of PEC components is still a real concern. This paper outlines a methodology for monitoring the degradation of PECs over the operational lifetime by utilizing a component level, physics based Digital Twin (DT). As well as providing a methodology for the real time comparison of parameters to realize the onset of faults subjected to operational stresses, the DT also provides a novel method of training a classifier by simulating the faults within the PEC, a process that in reality, would be difficult to achieve from a physical device. Feature extraction is via Wavelet Scattering and classification is provided using a Support Vector Machine (SVM) approach. The overall approach is one that is novel and expandable to a wide range of PEC topologies and will be beneficial to the optimization and maintenance of PECs in a variety of platforms.