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author:

Wang, Kang (Wang, Kang.) [1] | Wang, Guobin (Wang, Guobin.) [2] | Zeng, Jinglan (Zeng, Jinglan.) [3] | Liu, Bing (Liu, Bing.) [4] | Chen, Yangyang (Chen, Yangyang.) [5] | Shu, Shengwen (Shu, Shengwen.) [6]

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EI Scopus

Abstract:

The structure of a power transformer through the combination of oil and paper as the main insulation system is the key to its safe and reliable operation. In this paper, a grey correlation model is constructed by Support Vector Machine (SVM) from the perspective of the electric field change during the discharge process, and the model is optimized by using the idea of cross-validation to find the optimal parameters to construct the optimal transformer oil gap frequency breakdown voltage prediction model. In this paper, the SVM model is used to predict the transformer oil gap frequency breakdown voltage from the 30 min withstand voltage test, and the predicted breakdown voltage is compared with the actual breakdown voltage. The validity of the model was verified. © 2023 IEEE.

Keyword:

Electric breakdown Electric transformer testing Finite element method Forecasting Insulating oil Oil filled transformers Power transformers Support vector machines

Community:

  • [ 1 ] [Wang, Kang]Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou, China
  • [ 2 ] [Wang, Guobin]Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou, China
  • [ 3 ] [Zeng, Jinglan]Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou, China
  • [ 4 ] [Liu, Bing]Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou, China
  • [ 5 ] [Chen, Yangyang]Fuzhou University, College of Electrical Engineering and Automation, Fuzhou, China
  • [ 6 ] [Shu, Shengwen]Fuzhou University, College of Electrical Engineering and Automation, Fuzhou, China

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Year: 2023

Language: English

Cited Count:

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SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

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30 Days PV: 0

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