Multi-Objective Controller Design for Grid-Following Converters With Easy Transfer Reinforcement Learning

This article proposes an easy transfer reinforcement learning method that combines easy transfer learning (ETL) with deep reinforcement learning (DRL) to adapt a multiobjective controller tailor-made for one grid-following converter to other converters with different system parameters. The ETRL method contains five stages: system description; DRL learning; ETL; experimental data fine-tuning; and online implementation. The ETRL method can transfer knowledge effectively between controllers, offering a scalable solution for transferring knowledge between different converters without relying on extensive data or hyperparameter tuning. The ETRL method enhances controller adaptability, reduces training requirements by 96.4%, and ensures the stability of converter systems across diverse operating conditions. Experimental results validate the effectiveness of the proposed ETRL method, promising a new direction for power electronics controller design.