Learning to Topology Derivation of Power Electronics Converters with Graph Neural Network

Abstract

This paper proposes a general learning framework to derive topology of power electronics converters. To increase flexibility, a circuit is represented by a graph. A Graph Neural Network extract features of the circuit graph, which is further used in the RL framework. The topology derivation process is regarded as a Markov Decision Process. In each step, the RL agent selects and connects a new block to the initial block until a complete topology is made. To ensure that the derived circuits are feasible, basic circuit constraints are taken into consideration in the reward function. By using this framework, many new six-port, eight-port and ten-port converters are derived. Simulation results show that the derived circuits satisfy given constraints well.