[Objective] Deep learning models are widely used in transformer fault diagnosis due to their ability to learn underlying data patterns and construct hierarchical feature representations. However

their massive number of parameters

complex network topology

and high calculation and storage costs limit their practical application in fault diagnosis of power transformers. [Methods] To address the above issues

this study proposed a transformer fault diagnosis method based on multi-level sparse MobileNetV2. First

spindle-shaped and hourglass-shaped blocks were used to compactly improve the inverted residual blocks of the MobileNetV2 model

reducing parameter number and computational complexity from the model structure itself to achieve preliminary model sparsity. Second

a group-level pruning method based on dependency graph model was proposed. The coupled parameters in the model were grouped

and a group-level pruning optimization strategy based on L2 norm was designed to perform sparse training and pruning fine-tuning. This process removed redundant structures and parameters in the model

further reducing parameter number and computational complexity and enhancing model sparsity. Finally

an 8-bit symmetric uniform quantization and quantization-aware training method was proposed. The 32-bit high-resolution floating-point parameters in the model were quantized into 8-bit low-resolution integer parameters. Building on this

model inference was performed to further reduce the computational complexity and achieve multi-level model sparsity. [Results] The results of numerical experiments and performance evaluations showed that compared with the original MobileNetV2 model

the improved multi-level sparse model proposed in this study achieved a fault identification accuracy of 95.2%

while reducing the parameter number

computational complexity

and model size by approximately 73.5%

96.9%

and 68.8%

respectively. Moreover

the inference time for identifying 1 000 images was only 0.66 seconds. [Conclusion] The proposed method in this study effectively combines three types of individual sparsity methods: compact model improvement

model pruning

and parameter quantization. It achieves multi-level sparsity of deep learning models while maintaining high accuracy

effectively addressing the issue of over-parameterization caused by limited sample data in power transformer fault diagnosis and eliminating its adverse effects.