Research on Inrush Current Suppression of No-load Transformer Closing Based on Circuit Breaker Action Time Prediction

The inrush current produced by the no-load transformer closing is prone to cause problems such as differential protection device malfunction. Currently, circuit breaker controlled closing devices are used to suppress excitation inrush current. The key to achieving controlled closing of circuit breakers is to make them close at the optimal moment. This research suggests a no-load transformer shutting inrush current suppression technique based on circuit breaker action time prediction to address this problem. This approach builds a BP neural network model training set by taking into account variables such as control voltage and ambient temperature. Then optimizes the circuit breaker action time prediction results using the Grey Wolf Optimizer algorithm. Simulation of excitation inrush of no-load transformer under different closing modes is carried out. The proposed controlled closing method, based on the prediction of circuit breaker action time, is shown to be effective in reducing the peak excitation inrush current to 0.45 times the rated current. This method can achieve greater closing inrush suppression.