A Bayesian Filtering Approach to Power System Dynamic State Estimation

This study evaluates the performance of dynamic state estimation methods to enhance the stability and reliability of synchronous generator systems under varying operational conditions. The analysis focuses on the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF), and the Particle Filter (PF). Monte Carlo simulations are employed to assess the impact of sampling rates, noise characteristics, and transient faults on estimation accuracy. The results indicate that while EKF performs well under nominal conditions, its accuracy degrades significantly in high-noise environments. The UKF provides a balanced tradeoff between accuracy and computational complexity but exhibits limitations in extreme scenarios. Conversely, the PF demonstrates superior robustness against complex noise distributions, making it well-suited for systems with substantial uncertainties. These findings provide valuable insights into the selection of appropriate estimation techniques to ensure the reliable operation of synchronous generator systems.