Abstract
Dissolved gas analysis (DGA) is an effective method for oil-immersed transformer fault diagnosis. This paper proposes a transformer fault diagnosis method based on Stacked Contractive Auto-Encoder Network (SCAEN), which can detect the transformer’s internal fault by using DGA data, including H2, CH4, C2H2, C2H4, C2H6. The network consists of a three-layer stacked contractive auto-encoder (SCAE) and a backpropagation neural network (BPNN) with three hidden layers. A large amount of unlabeled data is used to train to obtain initialization parameters, and then a limited labeled dataset is used to fine-tune and classify the faults of trans-formers. The proposed method is suitable for transformer fault diagnosis scenarios, which contains very limited labeled data. when tested on real DGA dataset, the fault diagnosis accuracy is up to 95.31% by SCAEN, which performs better than other commonly used models such as support vector machine (SVM), BPNN, auto-encoder (AE), contractive auto-encoder (CAE) and SCAE.
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References
Mirowski, P., LeCun, Y.: Statistical machine learning and dissolved gas analysis: a review. IEEE Trans. Power Deliv. 27, 1791–1799 (2012)
Zahriah, B.S., Rubiyah, B.Y.: Support vector machine-based fault diagnosis of power transformer using k nearest-neighbor imputed DGA dataset. J. Comput. Commun. 02, 22–31 (2018)
Wang, Z.Y., Liu, Y.L., Griffin, P.J.: A combined ANN and expert system tool for transformer fault diagnosis. IEEE Trans. Power Deliv. 13, 1224–1229 (1998)
Yang, H.T., Liao, C.C., Chou, J.H.: Fuzzy learning vector quantization networks for power transformer condition assessment. Dielectr. Electr. Insul. IEEE Trans. 8, 143–149 (2001)
Seifeddine, S., Khmais, B., Abdelkader, C.: SVM-based decision for power transformers fault diagnosis using Rogers and Doernenburg ratios DGA. In: International Multi-conference on Systems, pp. 1–6 (2013)
Hinton, G.E.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)
Suck, B.R., Sung, K.O.: Design of fuzzy k-nearest neighbors classifiers based on feature extraction by using stacked autoencoder. Trans. Korean Inst. Electr. Eng. 64, 113–120 (2015)
Salah, R., Pascal, V., Xavier, M. et al.: Contractive auto-encoders: explicit invariance during feature extraction. In: ICML (2011)
Hadley, B.C., et al.: Mapping inundation uncertainty with a standard score (Z-score) technique, in Agu Fall Meeting (2010)
Duval, M., Depabla, A.: Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases. Electr. Insul. Mag. IEEE 17(31), 31–41 (2001)
Ali, A.: Dynamic assessment and prediction of equipment status in 220Â kV substations by big data and AI technology, http://dx.doi.org/10.21227/jve3-nh57. Accessed 26 May 26
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Zhong, Y., Hu, C., Lu, Y., Wang, S. (2021). Transformer Fault Diagnosis Based on Stacked Contractive Auto-Encoder Net. In: Liu, Q., Liu, X., Shen, T., Qiu, X. (eds) The 10th International Conference on Computer Engineering and Networks. CENet 2020. Advances in Intelligent Systems and Computing, vol 1274. Springer, Singapore. https://doi.org/10.1007/978-981-15-8462-6_57
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DOI: https://doi.org/10.1007/978-981-15-8462-6_57
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