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Air insulation strength relates closely to the electrostatic field distribution of the gap configuration. To achieve insulation prediction on the basis of electric field (EF) simulations, the spatial structure is characterized by a feature set including 38 parameters defined on a straight line between sphere electrodes. A support vector classifier (SVC) with particle swarm optimization (PSO) is used to establish a prediction model, whose input variables are those features. The EF nonuniform coefficient <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> of each sample gap is calculated and used for training sample selection according to the ranges of <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> values. Trained by only 11-sample data, the PSO-optimized SVC model is employed to predict the power frequency breakdown voltages of 260-sphere gaps with a wide range of structure sizes. The predicted values coincide with the standard data given in IEC 60052 very well, with the same trend and minor relative errors. The MAPEs of the five predictions with different training sets are within 2.0%. The model is also effective to predict the breakdown voltages of <inline-formula> <tex-math notation="LaTeX">$\Phi 9.75$ </tex-math></inline-formula>-cm sphere–<inline-formula> <tex-math notation="LaTeX">$\Phi 6.5$ </tex-math></inline-formula>-cm sphere gaps, whose MAPEs are within 2.6%. The results demonstrate the effectiveness of the EF feature set and the generalization ability of the SVC model under the case of limited samples. This paper lays the foundation for estimating the dielectric strength of other air gaps with similar structures.

A Feature Set for Structural Characterization of Sphere Gaps and the Breakdown Voltage Prediction by PSO-Optimized Support Vector Classifier