DOAJ Open Access 2022

Transformer Fault Diagnosis Method Based on DCAE-KSSELM

Lingling HAO Yongli ZHU Yongzheng WANG

Abstrak

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.

Topik & Kata Kunci

Electricity Production of electric energy or power. Powerplants. Central stations

Penulis (3)

Lingling HAO

Yongli ZHU

Yongzheng WANG

Format Sitasi

APA MLA BibTeX

HAO, L., ZHU, Y., WANG, Y. (2022). Transformer Fault Diagnosis Method Based on DCAE-KSSELM. https://doi.org/10.11930/j.issn.1004-9649.202111003

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Informasi Jurnal

Tahun Terbit: 2022
Sumber Database: DOAJ
DOI: 10.11930/j.issn.1004-9649.202111003
Akses: Open Access ✓