Real-time transient stability prediction using incremental learning algorithm

Real-time transient stability prediction is an essential and challenging step of response-based transient stability emergency controls. Machine learning methods including decision trees and artificial neural networks have the potential to be applied to the problem. To counter the inefficiency of common machine learning methods in learning new information, an incremental learning algorithm is employed to train an artificial neural network for real-time transient stability prediction. The resulted learning framework can readily be integrated into on-line dynamic security assessment. The effectiveness of such prediction model is demonstrated by the simulation results of a practical power system.