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Comprehensive Parameter Optimization Using an Empowered and Lightweight Surrogate Model
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AI
DOI：10.1109/TPEL.2024.3396504.png)
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Abstract
En 中文
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.
Keywords:
Optimization
Tuning
Phase locked loops
Power electronics
Genetic algorithms
Real-time systems
Computational modeling
Control parameter tuning
simulation
soft open point (SOP)
surrogate model
Journal
IF:
6.5
Papers:
1.7W
Citations:
8.3W