Power Electronics Converter's Health Management using Machine Learning

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This project will propose new techniques for assessing degradation of power electronics converters especially considering industrial drive applications. The main aim of this project is to develop health management techniques for industrial drives considering the use of wide bandgap power semiconductor devices. Predictive techniques such as machine learning will be utilised for condition monitoring and prognostics of semiconductor devices and DC-link capacitors in industrial motor drives. The target of this research is to ensure better reliability in industrial motor drives without compromising efficiency, cost, and operational limits. Life consumption prediction could enable intelligent performance derating to protect the hardware and reduce chances of a breakdown and plant shutdown.