Research On Prediction Method Of Power System Transient Disturbed Trajectory Based On Self-Remembering Model

Aiming at the transient stability disturbed trajectory prediction, a real-time prediction method of transient stability disturbed trajectory based on self-remembering gray Verhulst model is proposed. At the same time, considering the typical dynamic characteristics of the transient stability disturbed trajectory and its strong nonlinearity and stochastic volatility, a gray Verhulst model with self-remembering is constructed. Based on this mathematical prediction model, a real-time prediction scheme based on adaptive model parameters is proposed. The scheme adopts equal-dimensional innovation and rolling prediction method, which can make full use of WAMS real-time measurement data and continuously update the sample data to improve prediction performance. Model parameter adaptability. Finally, the rapidity and effectiveness of the transient instability identification based on the predicted trajectory are simulated and verified in the New England 10-machine 39-bus system. The results show that the method can effectively improve the rapidity of transient instability identification, which can be used for subsequent emergency. Control to get more time.