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

TL;DR

This paper introduces a robust, scalable dynamic state estimator for synchronous generators that guarantees performance bounds under uncertainties, improving accuracy over existing methods in power system dynamic state estimation.

Contribution

The paper develops an $ ext{L}_ ext{infty}$-based robust observer with theoretical error bounds for power system DSE, enhancing robustness and performance guarantees compared to prior approaches.

Findings

The estimator performs well under various uncertainties and operating conditions.

Theoretical bounds on estimation error are validated through numerical case studies.

The proposed method outperforms existing DSE techniques in accuracy and robustness.

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.