A Novel Gain Modeling Technique for LLC Resonant Converters based on The Hybrid Deep-Learning/GMDH Neural Network

TL;DR

This paper introduces a hybrid deep-learning and GMDH neural network approach to accurately model LLC resonant converter gain with simpler algebraic equations, enhancing design efficiency over traditional methods.

Contribution

The paper proposes a novel hybrid modeling technique combining deep learning and GMDH neural networks for LLC resonant converters, balancing accuracy and simplicity.

Findings

Hybrid model outperforms traditional methods like FHA.

Significant accuracy improvements across wide operating ranges.

Model simplifies converter design process.

Abstract

This paper presents a novel hybrid approach for modeling the voltage gain of LLC resonant converters by combining deep-learning neural networks with the polynomial based Group Method of Data Handling (GMDH). While deep learning offers high accuracy in predicting nonlinear converter behavior, it produces complex network models. GMDH neural networks, in contrast, yield simpler algebraic equations that can be more convenient in converter design. By training a deep network on data from an FPGA based real time simulator and then using the network s predictions to train a GMDH model, the proposed hybrid method achieves both high accuracy and design friendly simplicity. Experimental results show significant improvements over traditional methods such as First Harmonic Approximation (FHA) and frequency domain corrections, particularly for wide operating ranges.