Dynamic State Estimation for Microgrid Structures

Vieyra, Natanael; Maya, P.; Castro, Luis M.

doi:10.1080/15325008.2020.1758845

Cited by 8 publications

(11 citation statements)

References 27 publications

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“…Observability Property. Te observability condition (15) is the same as the one obtained in [27]; (14) shows almost no variations with respect to the preceding work [27] due to the diferences involved in the construction of the L-D estimation model (9) as mentioned in Remark 2.…”

Section: Load Angle Measurementmentioning

confidence: 67%

“…Dynamic system state estimators developed decades ago, and diferent local and centralized schemes are found in scientifc literature, many of which are based on the Extended Kalman (EKF) approach [7][8][9][10][11][12][13][14] and some of its variants [15][16][17][18][19][20][21][22]. Despite its robust performance (in the presence of parametric errors and measurement noise) and well-known constructive design, these dynamic estimators have a heuristic tuning, not to mention the lack of formal robust convergence proofs.…”

Section: Introductionmentioning

confidence: 99%

“…Despite its robust performance (in the presence of parametric errors and measurement noise) and well-known constructive design, these dynamic estimators have a heuristic tuning, not to mention the lack of formal robust convergence proofs. Also, for those designed under a centralized approach (Wide-Area) [7,14,21], there is a heavy computational load associated with integrating a set of nonlinear (NL) ordinary diferential equations (ODEs) (in a number that grows quadratically with the number of states). In contraposition, the NL sliding mode perturbation observer (SMPO) [23] is built based on a decentralized model variant of the NL MPS model, with additional (called fctitious) states that account for intermachine state interaction.…”

Section: Introductionmentioning

confidence: 99%

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Two Decentralized Dynamic State Estimation Schemes for Multimachine Power Systems with Transmission Losses

Vieyra

Maya‐Ortiz

Angeles‐Camacho

2023

International Transactions on Electrical Energy Systems

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This article addresses the problem of estimating the state of a multimachine power system (MPS). To work with power networks with lossy transmission lines, a variation of the classical third-order MPS is proposed by considering generators’ electrical power injected into the grid as a state variable. Based on a linear decentralized estimation model (tailored for a specific purpose), the state variables of generators (load angle, relative speed, and electrical power) together with terminal voltage magnitudes are estimated in a decentralized fashion through two new robustly convergent linear Luenberger estimators, one based on load angle measurement and the other based on relative speed measurement. The new MPS estimation model includes a set of robustly quick observable states, one per machine, which allows capturing the interaction with other generators, transmission line losses, unknown disturbances, and model errors. The result is a design superior to other related estimation techniques such as the extended Kalman filter (EKF) or Sliding Modes Perturbation Observer (SMPO) in terms of (i) a conventional-like simple pole placement-based tuning, (ii) low online computational load and disturbance rejection capability, and (iii) small gain-based convergence assessment. The performance of the proposed state estimation scheme is illustrated in a 3-machine power system under different operational conditions.

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Section: Load Angle Measurementmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Two Decentralized Dynamic State Estimation Schemes for Multimachine Power Systems with Transmission Losses

Vieyra

Maya‐Ortiz

Angeles‐Camacho

2023

International Transactions on Electrical Energy Systems

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“…2. 2) Use equation (20) to average the models recursively, as explained in Fig. 4 Wk+1 = 1 2 W SNR,λ best + Wk ∀k ∈ N (20)…”

Section: Recursive Average Modelmentioning

confidence: 99%

“…Other works have focused on scrutinizing the problem of dynamic state estimation for microgrids [19]. For instance, in [20] a modified extended Kalman filter (EKF) [21] was proposed to estimate the dynamic variables of generator units, as well as the static variables related to the network. In [22], a distributed dynamic state estimator was proposed to estimate the operation of the energy resources and the status (connected or inslanded) of the microgrid.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Machine Learning State Estimation Model for Unobservable Microgrids

et al. 2022

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An observable microgrid may become unobservable when sensors are at fault, sensor data is missing, or data has been tampered by malicious agents. In those cases, state estimation cannot be performed using traditional approaches without pseudo-measurements. To address the lack of observability, this article presents the design and implementation of a novel three-phase state estimation method for unobservable and unbalanced AC microgrids, using machine learning techniques, without pseudo-measurements, and under heteroscedastic (i.e., non-constant variance) noise. The proposed machine learning state estimation (MLSE) makes full use of multiple candidate models trained with a small number of power flow simulations via OpenDSS, through random levels of demand and renewable generation in every simulation, enhanced through a proposed Tikhonov regularization operator. To deal with the heteroscedastic nature of measurements, a recursive average model is proposed to accurately estimate the state variables. Results are obtained using real data from a microgrid located on the main campus of the State University of Campinas (UNICAMP), in Brazil. The method can be easily adapted to microgrids with different configurations, distributed energy resources, and measurements. It is shown that the proposed MLSE outperforms the traditional weighted least square (WLS) state estimator.INDEX TERMS Machine learning, microgrids, Moore-Penrose left pseudoinverse, Tikhonov regularization operator, recursive average model.

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Event-triggered control for switched singular systems with asynchronous switching and state jumps

Yang

2023

Applied Mathematics and Computation

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Dynamic State Estimation for Microgrid Structures

Cited by 8 publications

References 27 publications

Two Decentralized Dynamic State Estimation Schemes for Multimachine Power Systems with Transmission Losses

Two Decentralized Dynamic State Estimation Schemes for Multimachine Power Systems with Transmission Losses

Design and Implementation of a Machine Learning State Estimation Model for Unobservable Microgrids

Event-triggered control for switched singular systems with asynchronous switching and state jumps

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