A Multi-population genetic programming algorithm to model estimation for LLC resonant converter

An accurate model of the LLC resonant converter, serving as a critical energy conversion stage in new energy storage converters, plays a pivotal role in its optimized design. However, traditional modeling approaches often fail to achieve ideal performance in practical applications, particularly due to parasitic parameter effects that cause significant discrepancies between theoretical and actual voltage gain. To better capture the nonlinear characteristics of the circuit, this paper adopts a data-driven approach and introduces the Multi-Population Genetic Programming (MPGP) algorithm, which enhances model interpretability and clarity. MPGP employs a multi-population strategy to optimize the evolutionary process, improving search capability and ensuring the generation of more precise models even with limited training data. Experimental validation on four dataset groups of LLC resonant converter demonstrates that MPGP significantly outperforms first harmonic approximation (FHA), genetic programming (GP), and five state-of-the-art regression methods in terms of estimation accuracy, model visualization, and interpretability. Moreover, the algorithm strengthens support for power electronics converter design, contributing to improved optimization of power electronic systems.