Parameter Optimization of LLC-Converter with multiple operation points using Reinforcement Learning

The optimization of electrical circuits is a difficult and time-consuming process performed by experts, but also increasingly by sophisticated algorithms. In this paper, a reinforcement learning (RL) approach is adapted to optimize an LLC converter at multiple operation points corresponding to different, high-efficient output powers at different frequencies. During a training period, the RL agents extracts a problem specific optimization strategy enabling optimizations for any objective and boundary condition within a pre-defined range. The results show, that the trained RL agent is able to solve new optimization problems on this LLC system within 50 tuning steps for two operation points with efficiencies greater than 90%. Therefore, this AI technique provides the potential to augment expert-driven design processes with data-driven strategy extraction in the field of power electronics and beyond.