Version 1
: Received: 1 May 2023 / Approved: 2 May 2023 / Online: 2 May 2023 (01:27:29 CEST)
How to cite:
Huang, X.; Yuan, Y.; Li, J. A Review of Transformer Fault Diagnosis Based on Information System Theory and Machine Learning. Preprints2023, 2023050036. https://doi.org/10.20944/preprints202305.0036.v1
Huang, X.; Yuan, Y.; Li, J. A Review of Transformer Fault Diagnosis Based on Information System Theory and Machine Learning. Preprints 2023, 2023050036. https://doi.org/10.20944/preprints202305.0036.v1
Huang, X.; Yuan, Y.; Li, J. A Review of Transformer Fault Diagnosis Based on Information System Theory and Machine Learning. Preprints2023, 2023050036. https://doi.org/10.20944/preprints202305.0036.v1
APA Style
Huang, X., Yuan, Y., & Li, J. (2023). A Review of Transformer Fault Diagnosis Based on Information System Theory and Machine Learning. Preprints. https://doi.org/10.20944/preprints202305.0036.v1
Chicago/Turabian Style
Huang, X., Yumiao Yuan and Jingyu Li. 2023 "A Review of Transformer Fault Diagnosis Based on Information System Theory and Machine Learning" Preprints. https://doi.org/10.20944/preprints202305.0036.v1
Abstract
Safe, high-quality and economical electric energy transportation is the basic requirements of modern power system operation. Transformer, as one of the core equipment of power system and national grid system, has the characteristics of diverse types, variable models and wide deployment. It is the basic equipment for power system to realize voltage change and electric energy distribution. Because the power system transformer needs to operate with load for a long time, the probability of failure is usually higher than that of other power equipment in general. This paper starts from the transformer fault and fault diagnosis. Firstly, the transformer fault types, traditional transformer fault diagnosis techniques and the advantages of machine learning in transformer fault diagnosis are reviewed. Secondly, the application of information system theory and information entropy in transformer fault diagnosis is introduced. Then it introduces machine learning technology, feature selection and extraction technology in transformer fault diagnosis, and machine learning algorithms such as support vector machine and extreme learning machine for transformer fault diagnosis. Finally, the potential development trend of information system theory, information entropy and machine learning in transformer fault diagnosis is forecasted. The combination of transformer fault prediction and machine learning algorithm is helpful for power system maintenance personnel to accurately predict the running state of power equipment, and also provides a new method and technology for the safe and reliable operation and regular maintenance of power system.
Keywords
transformer fault diagnosis; information system theory; information entropy; machine learning
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
