Fault Diagnosis of Wireless Power Transfer System Based on the Innovative ConvNeXt Neural Network

Abstract This paper presents a fault diagnosis method for wireless power transfer (WPT) systems using an enhanced ConvNeXt neural network to achieve accurate and efficient fault identification. Focusing on the LCC-LCC type WPT systems, the proposed approach analyzes module components and their potential faults to construct a comprehensive fault set. Furthermore, both voltage and current data under various fault conditions are generated using Monte Carlo method, accounting for normal component value fluctuations. Then an improved ConvNeXt neural network is employed for fault classification, which offers advantages over traditional power electronics fault diagnosis methods by eliminating the need for additional circuit structures, thus reducing costs and circuit complexity. Compared to conventional approaches like BP neural networks and standard CNN, the proposed method achieves superior fault identification accuracy. Experimental results demonstrate that the enhanced ConvNeXt model attains a fault diagnosis accuracy of 98%, outperforming some existing techniques.