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Research on the optimization of transformer fault prediction method based on characteristic gas

Published: 31 July 2024 Publication History

Abstract

Oil-immersed transformer is the most widely used transformer at present, every year because of the oil-immersed transformer fire, Explosions and failures that cause secondary disasters are common. In recent years, domestic and foreign scholars have done a lot of research on the transformer characteristic gas content prediction, but less research on the interval prediction model based on accurate value. In this regard, this paper explores the interval prediction method based on the point prediction method, determines the GM (Grey Model) model as the basic model of the point prediction, and proposes the LGM interval prediction method. After verification, it is found that the LGM interval prediction method is more accurate, which can better meet the actual engineering needs, ensure the reliable operation of the transformer, and has good engineering application value.

References

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Cao D. 2005. Gas analysis and diagnosis and fault inspection in transformer oil. China Electric Power Press, Beijing, CN.
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Deng J. 2002. Theoretical basis of Grey. Huazhong University of Science and Technology Press, Wuhan, CN.
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Zhou C. 1994. Jane of the validity of the first data of the original data column in the gray system GM (1.1) model Single-only proof. Journal of Hechi University,14 (3): 31-32.
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Andrew W B., John A D. 2016. Method for Calculating Point Estimates and Standard Errors for Meta-Analysis from Reported Point Estimates, 95% Confidence Intervals, and P-Values. The FASEB Journal, 30(1): 1132-1154.
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Selim K. 2015. Confidence Interval. Journal of Mood Disorders, 5(2): 92-104.
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Chen M, Song B, Zheng H. 2009. Short-term forecast of the gas dissolved in power transformer using the hybrid grey model. Kybernetes, 38(3):489ཞ496.
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Chen G P, Shi Y J, She H Z. 2020. Application of improved GM (1, m) model for transformer faults prediction. IOP Conference Series: Earth and Environmental Science, 558(5): 052033.
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Qin J F, Zhou C, Li L L. 2019. Transformer Fault Prediction Method Based on Multiple Linear Regression. IOP Conference Series: Materials Science and Engineering, 486(1): 012037.
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Fauzi N A, N. H. Ali N, Ker P J. 2020. Fault Prediction for Power Transformer using Optical Spectrum of Transformer Oil and Data Mining Analysis. IEEE Access, 8: 136374-136381.

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

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    Publication History

    Published: 31 July 2024

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