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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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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