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Breakdown voltage of the air gap is of vital importance for the design of the external insulation in high-voltage transmission and transformation projects. In this paper, a new prediction method for the breakdown voltages of typical air gaps based on the electric field features and support vector machine (SVM) was proposed. According to the finite element calculation results of static electric field distribution, the electric field values in the whole region, discharge channel, surface of the electrode and the shortest path were extracted and post-processed, which constituted the electric field features characterizing the gap structure. Then, the breakdown voltage prediction model of the air gap was established by using electric field features as the input parameters to SVM, and whether the gap breakdown would happen as the output parameters of SVM, which changing the regression problem to a binary classification problem. This model was applied to predict the power frequency breakdown voltages of different short air gaps including sphere-sphere gaps, rod-plane gaps, sphere-plane gaps and sphereplane-sphere gaps. The power frequency breakdown voltages of longer air gaps which are affected by corona, and the 50% positive switching impulse breakdown voltages of long sphere-plane gaps and rod-plane gaps were predicted as well. The predicted results agree well with experimental values and simulated results of the published models, which validate the effectiveness of the proposed model. This method supplies a new possible way to obtain the breakdown voltage of air gaps.Index Terms -Air gap, breakdown voltage, electric field features, prediction, support vector machine (SVM).

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On-line monitoring and the diagnosis of the high-voltage circuit breaker (HVCB) have been discussed and investigated significantly in the past few decades. The vibration analysis is a noninvasive and advanced diagnostic technique suitable for the detection of mechanical conditions during the HVCB operation, which plays an important role in improving the operating reliability of HVCB and reduces maintenance costs. However, due to the very complicated mechanical system and extremely short operation time of HVCB, the vibration signal has the characteristics of highly nonlinear, non-stationary, and corrupted by heavy garbage noise, which makes it very difficult to precisely extract effective features for machinery fault diagnosis. To address this issue, an energy entropy of Hilbert marginal spectrum (HMS) based on variational mode decomposition (VMD) is presented to analyze the vibration signals of HVCB in this paper. The VMD is used to decompose the vibration signal into a set of intrinsic mode functions (IMFs) reflecting its local characteristics, and then, the energy entropy of IMF's HMS, which varies from different failure modes of HVCB, is obtained by Hilbert transform and entropy-information theory. The characteristics of IMF's HMS, which reveal the variation of vibration signals, under different failure modes of HVCB, are practically analyzed and examined to illustrate the advantage of the proposed method in feature extraction. The IMF that best reflects the mechanical anomaly information of HVCB is ascertained from IMF's HMS, and its Hilbert marginal spectrum energy entropy (HMSEE), namely, IMF-HMSEE, which synthetically reflects the variations of vibration signal's amplitude, phase, and frequency, is turned out to have excellent classification performance for some mechanical anomalies of HVCB. The effectiveness of the proposed approaches is substantiated by experiments carried out in a 12-kV vacuum HVCB.INDEX TERMS Online high voltage circuit breaker (HVCB) assessment, vibration analysis, machinery fault diagnosis, variational mode decomposition (VMD), Hilbert marginal spectrum energy entropy (HMSEE.