Adaptive Deep-Learning-Based Steady-State Modeling and Fast Control Strategy for CLLC DC-DC Converter in Highly Renewable Penetrated System

Abstract

The CLLC DC-DC converter offers highly efficient DC voltage conversion for the highly renewable penetrated system. The modeling significantly influences the performance of CLLC DC-DC converters. However, both the current FHA-based and time-domain analysis methods are incapable to incorporate different switching frequencies and load conditions. Moreover, due to the inevitable non-monotonic voltage gain, the PI controller with these models leads to the uncontrolled steady-state voltage deviation. As a novel modeling method, deep learning implicitly builds extensively accurate and arbitrary mappings, which can solve the aforementioned modeling problems. This paper proposes an adaptive steady-state modeling method for the CLLC converter based on deep learning. Precise voltage gain can be provided over a wide range of switching frequencies and load conditions. Besides, a fast control strategy based on the proposed modeling method is developed. This strategy searches the optimal operating point using particle swarm optimization, which rapidly adjusts voltage gain and suppresses the steady-state voltage deviation even in non-monotonic situations. Finally, a 400V/300V, 2.4kW SiC-based CLLC converter prototype with distributed heterogeneous controllers is implemented to verify the proposed methods. The experimental results show the accuracy of the proposed adaptive modeling and the effectiveness of the fast control strategy. The proposed method improves the stability and extends the operating range of the CLLC converter, which benefits the development of highly renewable penetrated systems.