Prediction of Breakdown Voltage of Long Air Gaps Under Switching Impulse Voltage Based on the ISSA-XGBoost Model

Zisheng Zeng, Bin Song (), Shaocheng Wu, Yongwen Li, Deyu Nie and Linong Wang
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Zisheng Zeng: Engineering Research Center of Ministry of Education for Lightning Protection and Grounding Technology, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Bin Song: Engineering Research Center of Ministry of Education for Lightning Protection and Grounding Technology, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Shaocheng Wu: Engineering Research Center of Ministry of Education for Lightning Protection and Grounding Technology, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Yongwen Li: Engineering Research Center of Ministry of Education for Lightning Protection and Grounding Technology, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Deyu Nie: Engineering Research Center of Ministry of Education for Lightning Protection and Grounding Technology, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Linong Wang: Engineering Research Center of Ministry of Education for Lightning Protection and Grounding Technology, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Energies, 2025, vol. 18, issue 7, 1-18

Abstract: In transmission lines, the discharge characteristics of long air gaps significantly influence the design of external insulation. Existing machine learning models for predicting breakdown voltage are typically limited to single gaps and do not account for the combined effects of complex factors. To address this issue, this paper proposes a novel prediction model based on the Improved Sparrow Search Algorithm-optimized XGBoost (ISSA-XGBoost). Initially, a comprehensive dataset of 46-dimensional electric field eigenvalues was extracted for each gap using finite element simulation software and MATLAB. Subsequently, the model incorporated a comprehensive set of input variables, including electric field eigenvalues, gap distance, waveform and polarity of the switching impulse voltage, temperature, relative humidity, and atmospheric pressure. After training, the ISSA-XGBoost model achieved a Mean Absolute Percentage Error (MAPE) of 7.85%, a Root Mean Squared Error (RMSE) of 56.92, and a Coefficient of Determination (R 2 ) of 0.9938, indicating high prediction accuracy. In addition, the ISSA-XGBoost model was compared with traditional machine learning models and other optimization algorithms. These comparisons further substantiated the efficacy and superiority of the ISSA-XGBoost model. Notably, the model demonstrated exceptional performance in terms of predictive accuracy under extreme atmospheric conditions.

Keywords: long air gap; breakdown voltage; electric field feature extraction; improved sparrow search algorithm; XGBoost (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
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