A Hybrid Fault Cluster and Thévenin Equivalent Based Framework for Rotor Angle Stability Prediction

This paper addresses a novel approach for rotor angle stability prediction in power systems. In the proposed framework, a fault cluster (FC) concept is introduced to divide an electrical network into several disparate zones. FCs are determined in accordance with the installed PMU locations so that the well-developed wide-area fault detection modules can estimate the origin of any fault in the network among FCs. The proposed framework assigns a stability prediction model to each FC. Parameters of the Thévenin equivalent network (TEN) seen from some generators, selected via a feature selection process, are calculated both in steady-state and during fault. The adopted TEN parameters are then applied as inputs to an ensemble decision tree-based prediction models. The proposed method benefits from parallel computation in the training process and does not require post-fault data. The performance of the proposed framework is validated on several IEEE test systems, followed by a discussion of results.