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Dynamic State Evaluation Method of Power Transformer Based on Mahalanobis–Taguchi System and Health Index

Author

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  • Yunhe Luo

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Xiaosong Zou

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Wei Xiong

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Xufeng Yuan

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Kui Xu

    (Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China)

  • Yu Xin

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Ruoyu Zhang

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

Abstract

Health status assessment is the key link of transformer-condition-based maintenance. The health status assessment method of power transformers mostly adopts the method based on the health index, which has the problems of multiple parameters of each component and strong subjectivity in the selection of weight value, which is easily causes misjudgment. However, the existing online monitoring system for dissolved gas in transformer oil (DGA) can judge the normal or abnormal state of the transformer according to the gas concentration in a monitoring cycle. Still, there are problems, such as fuzzy evaluation results and inaccurate judgment. This paper proposes a dynamic state evaluation method for power transformers based on the Mahalanobis–Taguchi system. First, the oil chromatography online monitoring time series is used to screen key features using the Mahalanobis–Taguchi system to reduce the problem of excessive parameters of each component. Then, a Mahalanobis distance (MD) calculation is introduced to avoid subjectivity in weight selection. The health index (HI) model of a single transformer is built using the MD calculated from all DGA data of a single transformer. Box–Cox transformation and 3 σ criteria determine the alert value and threshold value of all transformer His. Finally, taking two transformers as examples, we verify that the proposed method can reflect the dynamic changes of transformer operation status and give early warning on time, avoiding the subjectivity of parameter and weight selection in the health index, which easily causes misjudgment and other problems and can provide a decision-making basis for transformer condition-based maintenance strategies.

Suggested Citation

  • Yunhe Luo & Xiaosong Zou & Wei Xiong & Xufeng Yuan & Kui Xu & Yu Xin & Ruoyu Zhang, 2023. "Dynamic State Evaluation Method of Power Transformer Based on Mahalanobis–Taguchi System and Health Index," Energies, MDPI, vol. 16(6), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2765-:d:1098890
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    References listed on IDEAS

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    1. Mehran Tahir & Stefan Tenbohlen, 2021. "Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Frequency Response Measurements," Energies, MDPI, vol. 14(11), pages 1-25, May.
    2. Rahman Azis Prasojo & Harry Gumilang & Suwarno & Nur Ulfa Maulidevi & Bambang Anggoro Soedjarno, 2020. "A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation," Energies, MDPI, vol. 13(4), pages 1-20, February.
    Full references (including those not matched with items on IDEAS)

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