YAN Yan, ZHANG Siyi, LI Chen, WU Jiaqi, SHI Tingna
Open-circuit faults in gate drivers are typical failures of power devices, and accurate diagnosis of such faults can help improve the operational reliability of power electronic converters. Aiming at the deficiencies of existing fault diagnosis methods, a novel inverter power device gate driver open-circuit fault diagnosis method based on a DenseNet-ViT network is proposed: Firstly, normalization and augmentation processing are performed on sampled data to form three types of datasets: training set, validation set, and test set. Secondly, a DenseNet-ViT model is constructed to achieve enhanced extraction of fault features, and the model is trained using fault data. Finally, the validation set is used to conduct model testing, selecting the best-performing model. This method was applied to a three-level NPC inverter, detailing its modulation principle and the modeling process of the fault diagnosis model for this specific power converter topology, explaining the stochastic gradient descent function used in the model training process, and setting up an experimental platform for verification. Experimental results show that compared with other mainstream diagnostic methods, the proposed method has certain advantages in terms of floating-point computations, model parameters, and algorithm runtime.
