结合Diffusion-Based WGANGP的变压器油纸绝缘老化状态拉曼光谱检测方法

doi:10.3964/j.issn.1000-0593(2025)08-2164-10

Abstract
References
Related Citation (15)

Download: PDF (10917 KB)
Export: BibTeX | EndNote (RIS)

Abstract In this paper, a Wasserstein Generative Adversarial Network (WGANGP) method combining Raman spectroscopy with diffusion model improvement is proposed for improving the detection accuracy of the aging state of transformer oil-paper insulation. Raman spectroscopy, with its advantages of no contact and no loss, is used to assess the aging degree of a transformer by analysing the aging products of the oil-paper insulation material inside an oil-immersed power transformer. Combining deep learning classification models simplifies the Raman spectroscopy data preprocessing process, but such models have high requirements on the quantity and quality of training data. The long cycle of thermally accelerated aging experiments results in a relatively scarce set of valid Raman spectral data available for training, limiting the performance of the classification model. To address this challenge, a new data augmentation method, Diffusion-Based WGANGP, is introduced in this study. By combining the forward noise addition process of the denoising diffusion probabilistic model with WGANGP, the method introduces instantiated noise into WGANGP, removes the complex up-sampling process in the generator structure of the traditional WGANGP, simplifies the data augmentation model structure, and facilitates the optimization of model parameters. Compared with the traditional GAN and its variants, this method not only maintains the baseline drift trend of the original eigenpeak features associated with the aging degree in the Raman spectral dataset of the aging samples of the transformer oil-paper insulation, but also maintains an approximate spatial distribution of the features of the original dataset,, and the generated dataset with a Signal-to-Noise Ratio of 24.84 dB, which is improved by 32.11% compared to the original dataset; and at the same time, it also improves the diversity of generated samples, and enhances the generalisation ability, quantitative analysis ability and robustness of the aging diagnosis model based on deep learning. The experimental results show that the Raman spectral dataset generated using the Diffusion-Based WGANGP data augmentation model outperforms other data augmentation methods on several classification models, especially when combined with the ResNet-SVM classification model, in terms of Accuracy (0.997 4), F1 score (0.996 9), Recall (0.996 0) and Precision (0.998 0), which indicates that the improved data augmentation model can effectively solve the problem of the scarcity of samples of transformer aged insulating oil, and at the same time improves the classification model's ability to diagnose the aging state of the transformer quantitatively.

Key words：Transformer; Oil-paper insulation; Raman spectroscopy; Diffusion-Based WGANGP; Fault diagnosis

Received: 2024-10-18 Accepted: 2025-03-18

ZTFLH:

O433.4

Corresponding Authors: ZHANG Zhi-xian E-mail: zhangzhixian@cqut.edu.cn

Cite this article:

CHEN Xin-gang,AO Yi,ZHANG Zhi-xian, et al. Raman Spectroscopic Detection of the Aging State of Oil-Paper Insulation in Combined Diffusion-Based WGANGP Transformers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(08): 2164-2173.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2025)08-2164-10 OR https://www.gpxygpfx.com/EN/Y2025/V45/I08/2164

[1] Zhang E Z, Liu J, Zhang C H, et al. Energies, 2023, 16(3): 1396.
[2] Zhang Z X, Chen W G, Wu K J, et al. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 9517711.
[3] Soni R, Mehta B. Electric Power Systems Research, 2023, 214(B): 108954.
[4] Gouda O E, El Dein A Z. Electric Power Components and Systems, 2019, 47(4-5): 406.
[5] Feng D W, Chen G, Yan X Y, et al. IEEE Transactions on Dielectrics and Electrical Insulation, 2023, 30(6): 2758.
[6] Zheng H B, Yang E C, Wu S Y, et al. IEEE Transactions on Dielectrics and Electrical Insulation, 2022, 29(4): 1226.
[7] Kostina Y V, Rusakova O Y, Mikhalitsyn L A, et al. Petroleum Chemistry，2023, 63(1): 1.
[8] CHEN Xin-gang, CHEN Shu-ting, YANG Ding-kun, et al(陈新岗, 陈姝婷, 杨定坤, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(5): 1463.
[9] CHEN Xin-gang, FAN Yi-jie, MA Zhi-peng, et al(陈新岗, 范益杰, 马志鹏, 等). Laser & Optoelectronics Progress(激光与光电子学进展), 2023, 60(21): 2130002.
[10] Yang D K, Chen W G, Shi H Y, et al. High Voltage, 2021, 6(1): 51.
[11] Chen W G, Zou J X, Wan F, et al. AIP Advances, 2018, 8(3): 035204.
[12] Wu Y J, He Y Z, Wang Y L. Sensors, 2023, 23(16): 7153.
[13] Tang C H, Thu Q X, Wu W J, et al. Mathematical Problems in Engineering, 2020, 2020: 1245924.
[14] ZOU Jing-xin, CHEN Wei-gen, WAN Fu, et al(邹经鑫，陈伟根，万福，等). Transactions of China Electrotechnical Society(电工技术学报), 2018, 33(5): 1133.
[15] Nayak A A, Venugopala P S, Ashwini B, Archives of Computational Methods in Engineering, 2024, 31(8): 4739.
[16] Arjovsky M, Bottou L. 5 th International Conference on Learning Representations, 2017.
[17] Gulrajani I, Ahmed F, Arjovsky M, et al. Improved Training of Wassersetin GANs. Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017, USA(2017): 5767. doi: 10.48550/arXiv.1704.00028
[18] Shang Z W, Zhang J, Li W X, et al. Neural Processing Letter, 2023, 55(5): 6377.
[19] Yang R, Shu R, Nakayama H, IEICE Transactions on Information and Systems, 2022, E105 D(9): 1537.
[20] Ho J, Jain A, Abbeel P. Denoising Diffusion Probabilistic Models[C]. Advances in Neural Information Processing Systems 33 (NIPS 2020). Curran Associates, 2020, 6840. doi: 10.48550/arXiv.2006.11239.
[21] Song J M, Meng C L, Ermon S. Denoising Diffusion Implicit Models[C]. International Conference on Learning Representations (ICLR 2021), ICLR 2021, doi: 10.48550/arXiv.2010.02502.
[22] Kong Z, Ping W. On Fast Sampling of Diffusion Probabilistic Models[C]. International Conference on Learning Representations ( ICLR 2021), ICLR 2021, doi: 10.48550/arXiv.2106.00132.
[23] Jalbert J, Gilbert R, Denos Y, et al. IEEE Transactions on Power Delivery, 2012, 27(2): 514.
[24] DL/T 984-2018, Guide for the Diagnosis of Insulation Aging in Oil-Immersed Power Transformer(油浸式变压器绝缘老化判断导则)[S], 2018.