【目的】深度学习模型具有学习数据潜在规律和构建层次化特征表示的优势，因此被广泛应用于变压器故障诊断。然而，深度学习模型的参数量巨大、网络拓扑结构复杂且计算、存储成本高昂，限制了其在电力变压器故障诊断领域的应用。针对此问题，本文提出了一种多重稀疏MobileNetV2的变压器故障诊断方法。【方法】首先，使用纺锤块和沙漏块对MobileNetV2模型的倒残差块进行紧凑改进，从模型本身降低参数量和计算量，初步稀疏模型。其次，提出了一种基于依赖图模型的组级剪枝方法，对模型中的耦合参数进行分组，并基于L2范数设计组级剪枝优化策略，对模型进行稀疏训练与剪枝微调，删除模型中的冗余结构和参数，进一步降低参数量和计算量，实现模型稀疏化。最后，提出8位宽对称均匀量化和量化感知训练方法，将模型中的32位宽高分辨率浮点参数量化为8位宽低分辨率整型参数，并在此基础上进行模型推理，再次降低计算量，实现模型的多重稀疏化。【结果】数值试验和性能评估结果表明:与MobileNetV2模型相比，本文提出的多重稀疏化MobileNetV2模型在将故障识别精度提升至95.2%的前提下，将参数量、计算量和模型大小分别降低了约73.5%、96.9%和68.8%，且识别1 000张图片的推理时间仅为0.66秒。【结论】本文所提方法有效结合了紧凑改进、模型剪枝和参数量化三种单一稀疏化方法，在保证模型精度的前提下，实现了深度学习模型的多重稀疏化，较好地解决了电力变压器故障样本数据稀缺导致的模型过参数化问题，并消除了相关不利影响。

[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.

云南省自然科学基金资助项目(202303AA080002,202401AT070356)