Transformer Fault Diagnosis Method Based on DCAE-KSSELM

doi:10.11930/j.issn.1004-9649.202111003

Abstract

Abstract: In order to make full use of the large number of unlabeled samples generated during transformer fault and improve the accuracy of fault diagnosis, an innovative fault diagnosis method is proposed based on the combination of deep contractive autoencoder (DCAE) and kernel semi-supervised extreme learning machines (KSSELM). First, the unlabeled samples are used to train the DCAE network layer by layer and initialize the network parameters. Then the labeled samples are used to fine-tune the network parameters.Finally, the labeled samples and unlabeled samples are used as the inputs of the hybrid network of DCAE-KSSELM to make the fault diagnosis. The experimental results show that the proposed hybrid model has good stability, high fault diagnosis accuracy and strong robustness.

Key words: transformer, fault diagnosis, unlabeled sample, contractive autoencoder

HAO Lingling, ZHU Yongli, WANG Yongzheng. Transformer Fault Diagnosis Method Based on DCAE-KSSELM[J]. Electric Power, 2022, 55(2): 125-130.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202111003

https://www.electricpower.com.cn/EN/Y2022/V55/I2/125

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