Comprehensive Parameter Optimization Using an Empowered and Lightweight Surrogate Model

Fine tuning the parameters is crucial for achieving high-performance power electronics converters. Traditionally, iterative testing using professional simulation tools has been a common approach. However, running the simulation model is time consuming, and online parameter optimization generates parameters specific to each operating condition. In this article, we propose a novel approach that combines artificial intelligence (AI)-aided parameter tuning with simulation using a data-driven empowered surrogate model. The surrogate model is trained using a dataset derived from 3000 simulation tests, enabling rapid parameter tuning with feedback on system performance within a time frame of less than 0.1 ms, even on devices with restricted computational capabilities. Moreover, comprehensive parameter optimization for multiscenarios can be achieved using the surrogate model. A case study focusing on the parameter tuning of the soft-open-point is provided, including a comparison with AI-aided autonomous online parameter tuning methods. The results demonstrate the effectiveness and efficiency of the proposed approach.