High-Order Grid-Connected Filter Design Based on Reinforcement Learning

In grid-connected inverter systems, grid-connected filters can effectively eliminate harmonics. High-order filters perform better than conventional filters in eliminating harmonics and can reduce costs. For high-order filters, the use of multi-objective optimization algorithms for parameter optimization presupposes that the circuit structure must be known. To realize the design of the filter structure and related circuit parameters that meet the requirements of the grid-connected inverter system during the design process, this paper proposes a reinforcement learning (RL) method for designing higher-order filters. Our approach combines key domain knowledge with the characteristics of structural changes to obtain some constraints, which are then processed to obtain reward and are incorporated into RL strategy learning to determine the optimal structure and corresponding circuit parameters. The proposed method realizes the simultaneous design of parameters and structures in filter design, which greatly improves the efficiency of filter design. Simulation results for the corresponding grid-connected system setup show that the grid-connected filter designed by our method demonstrates a good performance in terms of filter dimension, harmonic rejection, and total harmonic distortion.