AUTHOR=He Lanfei , Li Lie , Li Ma , Li Zhiwei , Wang Xiao 

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

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.930093

DOI=10.3389/fenrg.2022.930093

ISSN=2296-598X

ABSTRACT=The reliability of high-capacity power transformer is fundamental to the stable operation of the power systems. However, characterizing the transformer aging process is a difficult task, considering the diverse aging factors in its life cycle. This prevents an effective management for 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. Combing with the average live of equipment, the extracted features are used as indicators for the transformer reliability evaluations. And we develop a deep learning-based approach using a convolutional neural network for the 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.