Breakdown voltage prediction in a point-barrier~plane air gap arrangement using self-organization neural networks

What is it about?

Air gap breakdown voltage prediction by mathematical calculations instead of experiments has been a long sought goal in the area of high-voltage engineering. In this chapter, a prediction method is proposed based on the electric field features and support vector machine (SVM). Two sets of electric field features are defined on the shortest interelectrode path of sphere-sphere and rod (sphere)-plane gap to characterize their spatial structures, which can be extracted from the electric field calculation results by finite element method (FEM). A breakdown voltage prediction model is established by SVM, while the input parameters are the electric field features, and the output parameters are −1 and 1, respectively, characterizing withstanding and breakdown of an air gap under the applied voltage. The proposed method is used to predict the power frequency breakdown voltages of IEC standard sphere-sphere air gaps and the switching impulse discharge voltages of large sphere-plane air gaps. The prediction results coincide well with the experimental data, the mean absolute percentage error of the 260 sphere-sphere gaps is within 2% and that of the 16 sphere-plane gaps is 3.2%. The results preliminarily validate the validity and accuracy of the proposed method for air gap breakdown voltage prediction.

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