A coherent generator group identification algorithm under extreme conditions

With the rapid development of large-scale regional interconnected power grids, the risk of cascading failures under extreme conditions, such as natural disasters and military strikes, has increased significantly. To enhance the response capability of power systems to extreme events, this study focuses on a method for generator coherency detection. To overcome the shortcomings of the traditional slow coherency method, this paper introduces a novel coherent group identification algorithm based on the theory of nonlinear dynamical systems. By analyzing the changing trend of the Euclidean norm of the state variable derivatives in the reduced system, the algorithm can accurately identify the magnitude of the disturbances. Based on the slow coherency methods, the algorithm can correctly recognize coherent generator groups by analyzing system characteristics under varying disturbance magnitudes. This improvement enhances the applicability and accuracy of the coherency detection algorithm under extreme conditions, providing support for emergency control and protection in the power system. Simulations and comparison analyses on IEEE 39-bus system are conducted to validate the accuracy and superiority of the proposed coherent generator group identification method under extreme conditions.