期刊
IET SCIENCE MEASUREMENT & TECHNOLOGY
卷 12, 期 7, 页码 886-892出版社
WILEY
DOI: 10.1049/iet-smt.2018.0058
关键词
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资金
- China Postdoctoral Science Foundation [2016M602354]
- Fundamental Research Funds for the Central Universities [2042017kf1009, 2042018kf0020]
- National Natural Science Foundation of China [51677138]
Electrostatic field distribution has a decisive influence on the strength of air insulation. To predict air-gap breakdown voltages according to electric field calculation results, a set of electrostatic field features is defined on the shortest interelectrode path of the rod-plane gap. These features are taken as the input parameters of a machine learning model established by support vector regression (SVR), thus to describe their relationships with the output breakdown voltage. Trained by small-sample data randomly selected according to the distribution ranges of the electric field non-uniform coefficient, the SVR model is applied to predict the breakdown voltages of rod-plane gaps with the rod diameter of 20, 25 and 30 mm, and the gap distance ranging from 1 to 9 cm. The predicted results agree well with the experimental data, while the mean absolute percentage errors of the six predictions are within 2.5%. Furthermore, the Phi 27 mm rod-plane gap breakdown voltages are also predicted, and the results fall in the range between those of Phi 25 and Phi 30 mm rod-plane gaps, which conform to the actual law. The results validate the validity of the electrostatic field features and the generalisation performance of the SVR model.
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