Optimizing DC Inductor Design with Air Gap for Triangular Excitation: A Reinforcement Learning Approach

Magnetic components play a crucial role in power electronics systems and exhibit a significant influence on their overall efficiency and performance. This paper presents an intelligent and fast method for optimizing the design of DC inductors. The proposed system is capable of generating a design for a DC inductor with an airgap within a time frame of less than one second, given the operating conditions of a triangular excitation current. The proposed approach is specifically founded in the Deep Deterministic Policy Gradient (DDPG) algorithm, one of the reinforcement learning (RL) techniques, which facilitates the agent’s ability to interact with its environment and acquire knowledge through iterative interactions. By using a single universal DDPG model, the magnetic design process with variant operating points can be substantially accelerated. The results indicate that the DDPG algorithm can effectively achieve optimal magnetic component design, demonstrating the potential of RL as a valuable tool for automating power electronics design.