Prediction of zero crossing point of fault current based on Bayesian estimation

Breaking capacity of circuit breakers can no longer meet the continuous rising demand of fault current in present new power systems. The phrase-controlled fault interruption technology based on zero crossing point prediction is an important solution. Zero crossing prediction method of fault current based on Bayesian estimation is studied in this paper. The fault current model through different processing strategies for the attenuation time constant of fault current is established， and three algorithms such as extended Kalman filter， standard Kalman filter and particle filter as well as their implementation are proposed. The prediction results of the algorithms through numerical simulation are compared and analyzed. The simulation analysis shows that the method of calculating attenuation time constant in advance and then using the standard Kalman filter for state estimation is superior in prediction time， accuracy and amount of calculation， and is suitable for the engineering practice of control and protection