Robust Dynamic State Estimation of Synchronous Machines with Asymptotic State Estimation Error Performance Guarantees

Abstract

A robust observer for performing power system dynamic state estimation (DSE) of a synchronous generator is proposed. The observer is developed using the concept of ${\mathcal{L}}_{\infty }$ stability for uncertain, nonlinear dynamic generator models. We use this concept to (i) design a simple, scalable, and robust dynamic state estimator and (ii) obtain a performance guarantee on the state estimation error norm relative to the magnitude of uncertainty from unknown generator inputs, and process and measurement noises. Theoretical methods to obtain upper and lower bounds on the estimation error are also provided. Numerical tests validate the performance of the ${\mathcal{L}}_{\infty }$-based estimator in performing DSE under various scenarios. The case studies reveal that the derived theoretical bounds are valid for a variety of case studies and operating conditions, while yielding better performance than existing power system DSE methods.

arXiv commentIEEE Transactions on Power Systems, In Press. V2: Fixed some typos in the appendix

References in corpus (4)

• Optimal PMU Placement for Power System Dynamic State Estimation by Using Empirical Observability Gramian
• Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines under Unknown Measurement Noise Statistics
• Nonlinear Model Reduction in Power Systems by Balancing of Empirical Controllability and Observability Covariances
• Characterizing the Nonlinearity of Power System Generator Models