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Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time-synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today's synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems and novel system monitoring, control and protection tools to achieve better reliability and resiliency. [ABSTRACT FROM AUTHOR]

This paper presents a robust dynamic state estimator for the synchronous generators with unknown parameters. The estimator uses a constrained iterated unscented Kalman filter to estimate the state variables and unknown parameters of a two-axis model of a synchronous generator. The developed estimator's performance is validated using simulations, where the estimator is subjected to arbitrary initialization and large parameter errors. The developed dynamic estimator can potentially be used not only to track the dynamic states but also to detect and identify changes in model parameters with little a priori knowledge about the parameters other than a broad range which can be specified via appropriate constraints. [ABSTRACT FROM PUBLISHER]