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

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