Gramian Angular Field–Gramian Adversial Network–ResNet34: High-Accuracy Fault Diagnosis for Transformer Windings with Limited Samples

Hongwen Liu, Kun Yang, Guochao Qian (), Jin Hu, Weiju Dai, Liang Zhu, Tao Guo, Jun Shi and Dongyang Wang
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Hongwen Liu: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Kun Yang: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Guochao Qian: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Jin Hu: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Weiju Dai: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Liang Zhu: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Tao Guo: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Jun Shi: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Dongyang Wang: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China

Energies, 2025, vol. 18, issue 16, 1-19

Abstract: Transformers are critical equipment in power transmission and distribution systems, and the condition of their windings significantly impacts their reliable operation. Therefore, the fault diagnosis of transformer windings is of great importance. Addressing the challenge of limited fault samples in traditional diagnostic methods, this study proposes a small-sample fault diagnosis method for transformer windings. This method combines data augmentation using the Gramian angular field (GAF) and generative adversarial networks (GAN) with a deep residual network (ResNet). First, by establishing a transformer winding fault simulation experiment platform, frequency response curves for three types of faults—axial displacement, bulging and warping, and cake-to-cake short circuits—and different fault regions were obtained using the frequency response analysis method (FRA). Second, a frequency response curve image conversion technique based on the Gramian angular field was proposed, converting the frequency response curves into Gramian angular summation field (GASF) and Gramian angular difference field (GADF) images using the Gramian angular field. Next, we introduce several improved GANs to augment the frequency response data and evaluate the quality of the generated samples. We compared and analysed the diagnostic accuracy of ResNet34 networks trained using different GAF–GAN combination datasets for winding fault types, and we proposed a transformer winding small-sample fault diagnosis method based on GAF-GAN-ResNet34, which can achieve a fault identification accuracy rate of 96.88% even when using only 28 real samples. Finally, we applied the proposed fault diagnosis method to on-site transformers to verify its classification performance under small-sample conditions. The results show that, even with insufficient fault samples, the proposed method can achieve high diagnostic accuracy.

Keywords: transformer; frequency response analysis method; fault identification; Gramian angular field; generative adversarial networks; residual network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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