A Prediction Method for the Surface Arc Inception Voltage of Epoxy Resin Based on an Electric Field Feature Set and GS-SVR

Yihong Lin, Dengfeng Wei, Zhiwen Zhang, Zhaoping Ye, Wenhua Huang and Shengwen Shu ()
Additional contact information
Yihong Lin: Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007, China
Dengfeng Wei: Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007, China
Zhiwen Zhang: Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007, China
Zhaoping Ye: Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007, China
Wenhua Huang: School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Shengwen Shu: School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China

Energies, 2025, vol. 18, issue 22, 1-18

Abstract: To address the critical challenges posed by the complex coastal climate on the external insulation of electrical equipment, research into the prediction of the surface arc inception voltage of epoxy resin under multiple conditions is of great significance for preventing failures and guiding operations and maintenance. In this regard, we propose a prediction method for surface arc inception voltage based on grid search-optimized support vector regression (GS-SVR). Using a 21-dimensional electric field feature set along the shortest inter-electrode path as model input, high-accuracy prediction of surface arc inception voltage under complex conditions is achieved. The results demonstrate that the model accurately predicts surface arc inception voltage with limited samples, achieving a mean absolute percentage error (MAPE) of 6.24%. Furthermore, the non-uniform coefficient-based dataset partitioning method improves prediction accuracy compared to random partitioning, with the lowest MAPE of only 2.39%. The findings provide theoretical and technical support for improving the anti-pollution flashover and anti-condensation performance of epoxy resin insulating materials.

Keywords: electric field parameters; support vector regression (SVR); voltage prediction; grid search (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/22/5936/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/22/5936/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:22:p:5936-:d:1792295

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().