中圖分類號(hào)：TP306+.3 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.223320
中文引用格式： 黃文禮，茆驥，張銀勝，等. 基于深度學(xué)習(xí)的變壓器故障信號(hào)識(shí)別算法[J]. 電子技術(shù)應(yīng)用，2023，49(3)：54-60.
英文引用格式： Huang Wenli，Mao Ji，Zhang Yinsheng，et al. Deep learning based transformer fault signal recognition algorithm[J]. Application of Electronic Technique，2023，49(3)：54-60.

Deep learning based transformer fault signal recognition algorithm

Abstract： Abstract： In view of the complex structure and high maintenance cost of transformers, this paper proposes a transformer fault signal recognition algorithm based on deep learning. Firstly, the voiceprint signal under the working condition of the transformer is analyzed and the two-dimensional image signal is converted. Based on the advantages of VGG16 neural network in the image, a MCA attention mechanism is proposed, which can retain both background information and detail information. Secondly it optimizes the maximum pooled down sampling in VGG16, and adopts a soft pooled sampling method to reduce the feature loss caused by the maximum pooled down sampling in the image. Finally, in order to avoid the occurrence of over fitting, the activation function in the top structure of VGG16 is optimized, and the SELU activation function that can be self normalized is quoted. The experiment proves that the generalized S-transform is the best choice for converting one-dimensional time-domain signal to two-dimensional image signal, and the average recognition rate of the proposed algorithm for six types of fault signals reaches 99.15%.

Key words : VGG16 network；generalized S-transform；transformer fault signal