Transient Instability Prediction Using Decision Tree Technique

This paper presents a decision tree based method for out-of-step prediction of synchronous generators. For distinguishing between stable and out-of-step conditions, a series of measurements are taken under various fault scenarios including operational and topological disturbances. The data of input features and output target classes are used as the input-output pairs for decision tree induction and deduction. The merit of decision tree based detection of transient instability lies in robust classification of new unseen samples. The performance of the proposed method is verified on two test cases including a 9-bus dynamic network and the practical 1696-bus Iran national grid. The simulation results are presented for various input features and learning parameters.