A New Dynamic State Estimation Approach Including Hard Limits on Control Devices

Fuerte‐Esquivel, Claudio R.; Romay, Omar; Martínez-Parrales, R.; Acha, E.; Zamora-Cárdenas, Enrique A.

doi:10.1109/tpwrs.2022.3163196

Cited by 4 publications

(3 citation statements)

References 47 publications

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“…This error, in general, also depends on the voltage evolution between the measurement samples; however, for deriving the optimal recursive filter, we ignore these attributes. In (14), conventional measurement devices, such as remote terminal units, are not considered as their slow scan rate and the lack of synchronization hinder their employment during transient conditions [16]. Formulation (12) subsumes several one-step discretization methods, such as the explicit Euler, the implicit Euler, and the trapezoidal rule.…”

Section: B Power System Dynamic State Estimationmentioning

confidence: 99%

“…An extension to this observer to include also renewables generation is provided in [13]. Reference [14] shows that inequality constraints can be explicitly incorporated into the least squares estimation by reformulating them into equality constraints using complementarity theory. Finally, in our previous work [15], we address the problem in a movinghorizon fashion and deal with equality constraints and an incomplete power system model, albeit with a substantial computational burden.…”

Section: Introductionmentioning

confidence: 99%

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Moving-Horizon State Estimation for Power Networks and Synchronous Generators

Katanic

Lygeros

Hug

2022

2022 North American Power Symposium (NAPS)

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Power systems are highly-complex, large-scale engineering systems subject to many uncertainties, which makes accurate mathematical modeling challenging. This paper proposes a novel, centralized dynamic state estimator for power systems that lack models of some components. Including the available dynamic evolution equations, algebraic network equations, and phasor measurements, we apply the least squares criterion to estimate all dynamic and algebraic states recursively. This approach results in an algorithm that generalizes the iterated extended Kalman filter and does not require static network observability. We further derive a graph theoretic condition to guarantee estimability for the proposed approach. A numerical study evaluates the performance under short circuits in the network and load changes for three different discretization schemes. The results show superior tracking performance compared to robust twostage procedures within computational times that are feasible for real-time application.

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Section: B Power System Dynamic State Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Moving-Horizon State Estimation for Power Networks and Synchronous Generators

Katanic

Lygeros

Hug

2022

2022 North American Power Symposium (NAPS)

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show abstract

“…Power system state estimation (PSSE) can obtain the states of power system, which is fairly important for reliable operation of future power systems [4]. The traditional measurements for PSSE are usually collected from the supervisory control and data acquisition (SCADA) system, which has low sampling rates and second-level communication delays [5]. With the extensive deployment of advanced facilities, the phasor measurement unit (PMU) based on Global Positioning System has higher sampling rate and measurement accuracy, and can capture the dynamic characteristics of power system [6,7].…”

Section: Introductionmentioning

confidence: 99%

A distributed multi-area power system state estimation method based on generalized loss function

Chen

Liu

et al. 2023

Meas. Sci. Technol.

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For power system state estimation, the measurement noise is usually assumed to follow the Gaussian distribution and the widely used estimator is the weighted least squares (WLS). However, the Gaussian distribution assumption is not always true and the performance of WLS becomes bad when the measurement noise is non-Gaussian. In this paper, a new distributed state estimation (SE) method is proposed for multi-area power systems. The proposed distributed method is based on the generalized loss function so that it can reduce the influence of non-Gaussian noise. Further, thanks to the matrix-splitting technology, the proposed distributed method can be implemented in a distributed way so that the computation time in each local area can be reduced. The simulation results carried out in the IEEE 30-bus and 118-bus systems verify the robustness and effectiveness of the proposed distributed SE method.

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Recursive dynamic state estimation for power systems with an incomplete nonlinear DAE model

Katanic,

Lygeros,

Hug

2024

IET Generation Trans & Dist

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Power systems are highly complex, large‐scale engineering systems subject to many uncertainties, which makes accurate mathematical modeling challenging. This article introduces a novel centralized dynamic state estimator designed specifically for power systems where some component models are missing. Including the available dynamic evolution equations, algebraic network equations, and phasor measurements, the least squares criterion is applied to estimate all dynamic and algebraic states recursively. The approach generalizes the iterated extended Kalman filter and does not require static network observability, relying on the network topology and parameters. Furthermore, a topological criterion is established for placing phasor measurement units (PMUs), termed topological estimability, which guarantees the uniqueness of the solution. A numerical study evaluates the performance under short circuits in the network and load changes and shows superior tracking performance compared to robust procedures from the literature with computational times in accordance with the typical PMU sampling rates.

show abstract

A New Dynamic State Estimation Approach Including Hard Limits on Control Devices

Cited by 4 publications

References 47 publications

Moving-Horizon State Estimation for Power Networks and Synchronous Generators

Moving-Horizon State Estimation for Power Networks and Synchronous Generators

A distributed multi-area power system state estimation method based on generalized loss function

Recursive dynamic state estimation for power systems with an incomplete nonlinear DAE model

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