This article proposes a methodology for air gap breakdown voltage (BV) prediction based on support vector regression (SVR), taking various features extracted from the electrostatic field calculation results as input variables. The genetic algorithm (GA) is applied for feature selection to improve the performance of the SVR model. A case study on sphere gap BV prediction is reported to demonstrate the validity of the proposed technique. This study provides a reference for dielectric strength prediction by artificial intelligence algorithms, thus to guide the optimal design of air insulation structures.

中文翻译：

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基于支持向量回归的球间隙击穿电压预测的静电场特征选择技术

本文提出了一种基于支持向量回归（SVR）的气隙击穿电压（BV）预测的方法，以从静电场计算结果中提取的各种特征作为输入变量。遗传算法（GA）用于特征选择，以提高SVR模型的性能。报道了一个关于球隙BV预测的案例研究，以证明所提出的技术的有效性。该研究为人工智能算法预测介电强度提供了参考，从而为空气绝缘结构的优化设计提供了指导。