Deep Symbolic Optimization for Electric Component Sizing in Fixed Topology Power Converters

Ruben Glatt, Felipe Leno da Silva, VAN HAI BUI, Can Huang, Lingxiao Xue, Mengqi Wang, Fangyuan Chang, Yi Murphey, Wencong Su

Keywords: Reinforcement Learning, Symbolic Optimization, Power Converter

TL;DR: Optimizing power converter topologies based on deep symbolic optimization to improve power converter design.

Abstract: Power converters (PC) are a major component in any current electronic hardware device. The development and design is usually guided by expert knowledge and heavily relies on human intuition and experience. The process is a very time consuming and costly activity and it is generally hard to improve upon current designs. As a first step towards autonomous PC design, we are here proposing a new framework for the sizing of components for fixed topology PCs based on given design requirements. To this end, we developed surrogate models for rapid evaluation of new topologies and adapt the deep symbolic optimization (DSO) framework to generate new topologies guided by a reinforcement learning training signal. In an empirical evaluation, we show that our DSO based approach is able to find the optimal configuration for all investigated topologies, while reducing the learning time by at least a factor of 100 compared to popular RL algorithms.