基于格拉姆角差场和CNN-BiGRU的变压器故障识别法

doi:10.16180/j.cnki.issn1007-7820.2025.04.011

Abstract

Abstract:

In view of the problems such as the difficulty in extracting the fault characteristics of transformer windings and the relatively low diagnostic accuracy, this study proposes a transformer fault identification method based on the GADF(Gramian Angular Difference Field) and the CNN-BiGRU(Convolutional Neural Network-Bidirectional Gated Recurrent Unit) on the basis of the frequency response curve. In response to the problem that the original features have a small discriminative ability for different fault types, a moving window calculation method is proposed to process the sample segments. By combining with the Gram angular difference field transformation, the spectral features are obtained, realizing the mapping of one-dimensional data into three-dimensional image data. The distribution characteristics of different fault types in the spectral features are analyzed. Taking the obtained spectral features as the input, the fault segment data are classified through the recurrent convolutional neural network to obtain the identification results. Compared with the traditional methods, the proposed method has more obvious feature differences, and the accuracy is further improved. The simulation results show that the classification accuracy of the slices reaches 96.2%, and the high accuracy of the diagnostic results verifies the feasibility of this method.

Key words: transformer, fault diagnosis, Gramian angle difference field, spectrum signature, deep learning, recurrent convolutional neural network, high-dimensional spatial features

CLC Number:

TN99

XU Yaobo, YANG Xinqiang, XU Guangchao, YANG Shihao, DUAN Guoyong. Transformer Fault Identification Method Based on Gramian Angle Difference Field and CNN-BiGRU[J].Electronic Science and Technology, 2025, 38(4): 73-79.

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References 18

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故障诊断模型	识别准确率/%
CNN-BiGRU	75.6
GADF-CNN	92.6
GADF-BiGRU	81.3
GADF-CNN-BiGRU	96.2

故障诊断模型	识别准确率/%
SVM	88.9
CNN-BiGRU	96.2
RF	92.6
DNN	81.3
KNN	88.9

Transformer Fault Identification Method Based on Gramian Angle Difference Field and CNN-BiGRU

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故障类型	图像样本数量/个
匝间短路	36
线圈断股	36
金属异物	36
轴向扭曲	36
径向内凹	36
径向外凹	36

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