Comprehensive Parameter Optimization using Empowered and Lightweight Surrogate Model

Fine-tuning parameters is crucial for achieving highperformance 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 paper, we propose a novel approach that combines 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 0.1ms, even on devices with restricted computational capabilities. Moreover, comprehensive parameter optimization for multi-scenarios can be achieved using the surrogate model. A case study focusing on the parameter tuning of the soft open point (SOP) is provided, including a comparison with AIaided autonomous online parameter tuning methods. The results demonstrate the effectiveness and efficiency of the proposed approach