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 L∞ 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 L∞-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.

Combined finite‐time state feedback for high‐speed train systems with time‐varying delays and disturbances

“…37 Weights of real-time item 𝛼 i is chosen as 0.3 and weights of time-delay terms 𝛽 i are considered as 0.7. Based on data in Table 2, the rest of parameters in (14) are obtained as follows. where 𝛿(t) = 0.5|sin(0.002t − 0.1)|, 𝛿 = 0.8.…”

“…13 Based on the concept of L ∞ stability, a robust observer for performing power system dynamic state estimation (DSE) of a synchronous generator is proposed. 14 A novel nonlinear moving horizon estimation scheme and the corresponding approximate moving horizon estimation scheme are developed to cope with the state estimation task. 15 Compared to the literature of Wang, 16 time-varying delay instead of fixed time delay is considered in high-speed train systems.…”

“…For nonlinear discrete dynamic models with estimate constraints, nonlinearly transformed interference and noise, an online‐based adaptive filter is presented for state filtering and prediction 13 . Based on the concept of $$$ {L}_{\infty } $$$ stability, a robust observer for performing power system dynamic state estimation (DSE) of a synchronous generator is proposed 14 . A novel nonlinear moving horizon estimation scheme and the corresponding approximate moving horizon estimation scheme are developed to cope with the state estimation task 15 .…”

Non-Parametric Statistical Analysis of Current Waveforms through Power System Sensors

“…The authors in [4] explore the various ways in which PMU errors affect different applications such as the power system disturbance location, oscillation detection, island detection, and dynamic line rating. Analysis of PMU errors for state estimation is also a burgeoning field of study [5][6][7][8].…”

PMU‐based decentralised mixed algebraic and dynamic state observation in multi‐machine power systems

“…Based on these advancements, recent efforts have been made to develop novel DSE methods based on state observer design concepts for nonlinear systems with provable convergence guarantees for the estimation errors. In [17], a robust observer providing performance guarantees for the state estimation error norm against worst‐case disturbances is proposed based on the Lipschitz property of the employed synchronous generator model and PMU measurements. However, the paper focuses only on the estimation of the generator states and considers the exogenous input signals, i.e.…”