Investigation of Arcing Time Prediction for Secondary Arc Based on Statistical Theory and Multivariable Regression Algorithms

Abstract

Research on the secondary arc has long been important in the development of ultrahigh-voltage (UHV) transmission technology. Predicting the arcing time of secondary arc is important for single-phase automatic reclosing of the circuit breaker to protect the power system. In response to this problem, this study uses a multifactor variance model to select factors that significantly influence the arcing time and verifies its validity through residual analysis. The results show that the parameters of the circuit, such as wind direction, recovery voltage, secondary current, and arc length, significantly influence the arcing time. The density-based clustering algorithm (DBSCAN) is then used to remove outliers form the data, and the influence of each factor on the arcing time is analyzed through scatterplots. Finally, linear multivariate regression and nonlinear Gaussian process regression are used to fit the results. The results show that the linear regression has a good imitative effect. The proposed method is accurate and provides novel means of predicting the arcing time.