Data-driven Model-based Smart Control of Intelligent Gate Drive for Converter Operational Performance Improvement

Intelligent gate drive with tunable drivability to achieve optimal switching performance tradeoff can improve converter operational performance, especially for high-speed wide bandgap-based applications. It is equally important to design a controller to determine the suitable control strategy and then utilize the full capability of the developed intelligent gate drive circuitry. This paper proposed a data-driven switching model using machine learning techniques. The derived model depends on limited dataset to establish the correlation between operating conditions, drivability variables, and key switching data. With this model, a smart controller is designed to set the optimal drivability variable(s) of the intelligent gate drive depending on the real-time operating condition to optimize the switching performance. Finally, using the proposed smart controller along with a digital gate drive capable of gate impedance regulation, a case study with the SiC-based solar inverter is performed to validate its feasibility and effectiveness for converter operational performance improvement.