Heat Generation and Failure in Padmount Transformers Due to Zero Sequence Saturation

Fox, J. Curtiss; Hadidi, Ramtin; LaFlair, Nancy; Leonard, Jesse; Hodges, Joseph T.

doi:10.1109/tia.2019.2928252

Cited by 2 publications

(1 citation statement)

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“…Transformers are the current representative of large-power equipment, which include multiple failure forms such as discharge, overheating, or both (Castro and Miranda, 2005;Jongen et al, 2007;Zheng et al, 2012;Fox et al, 2019). The reasonable life estimation of the transformer also belongs to the category of fault prediction.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach to the Transformer Life Prediction Considering Diverse Aging Factors

He¹,

Li³

et al. 2022

Front. Energy Res.

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The reliability of a high-capacity power transformer is fundamental to the stable operation of power systems. However, characterization of the transformer aging process is a difficult task, considering the diverse aging factors in its life cycle. This prevents effective management of such equipment. In the work, we study the aging phenomenon of power system transformers, whose representative degeneration variables are extracted from real transformer operational data. Combining with the average life of the equipment, the extracted features are used as indicators for the transformer reliability evaluations. We developed a deep learning–based approach using a convolutional neural network for effective equipment life prediction. The performance of the transformer life prediction model is verified using field-test data, which demonstrates the superior accuracy of the presented approach.

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Section: Introductionmentioning

confidence: 99%