Comprehensive Parameter Optimization Using an Empowered and Lightweight Surrogate Model

• Published in: IEEE transactions on power electronics 2024-10, Vol.39 (10), p.12124-12129
• Main Authors: Yang, Qifan, Huang, Dihong, Chen, Yong, Dai, Ningyi
• Format: Article
• Language: English
• Subjects: Computational modeling; Control parameter tuning; Genetic algorithms; Optimization; Phase locked loops; Power electronics; Real-time systems; simulation; soft open point (SOP); surrogate model; Tuning
• ISSN: 0885-8993; 1941-0107
• DOI: 10.1109/TPEL.2024.3396504

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.