Power Electronics Converter's Health Management using Machine Learning

该项目将提出评估电力电子转换器退化的新技术，特别是考虑到工业驱动应用。该项目的主要目的是为考虑使用宽带隙功率半导体器件的工业驱动器开发健康管理技术。机器学习等预测技术将用于工业电机驱动器中半导体器件和直流电容的状态监测和预测。这项研究的目标是在不影响效率，成本和操作限制的情况下，确保工业电机驱动器更好的可靠性。寿命消耗预测可以实现智能性能降额，以保护硬件并减少故障和工厂关闭的可能性。

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.