Prediction of synchronous closing time of permanent magnetic actuator for vacuum circuit breaker based on PSO-BP - Hou, Chunguang; Yu, Xiao; Cao, Yundong; Lai, Changxue; Cao, Yuchen (IEEE transactions on dielectrics and electrical insulation, 2017)
One of the key points of phase-controlled technology is to predict the circuit breaker's operation time. In this paper, it took control voltage and ambient temperature as the main input variables to forecast the closing time through building backward propagation (BP) neural network model. In view of the high accuracy requirements for the closing time's prediction, it is difficult to meet the requirement by relying solely on the BP neural network. Basing on BP neural network, it uses particle swarm optimization (PSO) algorithm to optimize the model for improving the prediction accuracy. Through calculation and analysis, the PSO-BP algorithm is more accurate than BP neural network in the prediction accuracy, which controls the error within 0.2%. The prediction error meets the requirements of synchronous controlled technology.