Abstract
Abstract The Intelligent Power Module (IPM) has the integrated packaging, leading to the advantages of convenient use, safety and reliability. However, once it fails, it will cause the whole power supply system to be inoperative, and it is necessary to perform online Condition Monitoring (CM) of the IPM. In this paper, we extract the aging characteristics such as dynamic equivalent resistance, peak-to-peak value, switching frequency, and turn-off time only from the voltage and current of IPM drain–source port, and then propose a non-intrusive online CM method for the IPM based on the transformer neural network (TNN). We analyse the internal aging mechanism of the IPM, the changing law of aging features, and construct multi-dimensional aging fusion features, and then the TNN model is used to monitor early parameters drift of multi-dimensional fusion feature vectors and realize the accurate online prediction of IPM health condition. The experimental analysis results show that the fault prediction accuracy reaches 96%, and can realize the health CM under the condition of noise interference, weak aging features and few external observable points.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
