<scp>Multi‐area</scp>
            state estimation in a distribution network using
            <scp>Takagi‐Sugeno</scp>
            model estimated by Kalman filter

Ghadikolaee, Ebad Talebi; Kazemi, A.; Shayanfar, Heidarali

doi:10.1002/2050-7038.12466

Cited by 5 publications

(5 citation statements)

References 33 publications

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“…The final results are compared to the integrated WLS method, and it is shown that execution time and computational complexity are improved in a data-based SE method. In [97], a novel machine learning-based distributed state estimation is proposed. Mathematical formulations of a data which exchanged in the boundary area are analyzed, and a new machine learning-based supervised learning is designed.…”

Section: Distribution State Estimation: Data-driven Approachesmentioning

confidence: 99%

A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges

et al. 2022

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This paper provides a comprehensive review of distribution system state estimation in terms of basic definition, different methods, and their application. In the last few years, the operation of distribution networks has been influenced by the installation of distributed generations. In order to control and manage an active distribution network’s performance, distribution system state estimation methods are introduced. A transmission system state estimation cannot be used directly in distribution networks since transmission and distribution networks are different due to topology configuration, the number of buses, line parameters, and the number of measurement instruments. So, the proper state estimation algorithms should be proposed according to the main distribution network features. Accuracy, computational efficiency, and practical implications should be considered in the designing of distribution state estimation techniques since technical issues and wrong decisions could emerge in the control center by inaccurate distribution state estimation results. In this study, conventional techniques are reviewed and compared with data-driven methods in order to highlight the pros and cons of different techniques. Furthermore, the integrated distribution state estimation methods are compared with the distributed approaches, and the different criteria, including the level of area overlapping execution time and computing architecture, are elaborated. Moreover, mathematical problem formulation and different measuring methods are discussed.

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Section: Distribution State Estimation: Data-driven Approachesmentioning

confidence: 99%

A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges

et al. 2022

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“…Notably, this paradigm has found its way into the realm of smart grids [8], [9], leading to considerable speed enhancements in state estimation for large-scale systems [10]. Moreover, gradient-based techniques, often employing the Gauss-Newton method within each area, have been explored [11], along with the deployment of the Kalman filter [12]. While these advancements have undoubtedly marked progress, many existing multi-area state estimation methods rely heavily on geographical information for delineating network areas, often overlooking a distinct approach to zone partitioning.…”

Section: Introductionmentioning

confidence: 99%

A parallel approach for ultra-fast state estimation in large power system using graph partitioning theory

Karim Sarmadi,

Salehi Dobakhshari

2024

Acta IMEKO

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This paper introduces a novel approach for multi-area state estimation in large transmission networks through the application of graph partitioning theory. By harnessing the eigenvalues and eigenvectors of the Laplacian matrix, a large-scale transmission network is partitioned into manageable sections. Within these partitions, state estimation processes run in parallel, markedly improving efficiency compared to conventional methods. Linear state estimation is employed within each area, expediting computations and making it adaptable to large-scale networks, which traditionally pose computational challenges. The method's efficacy is demonstrated through comprehensive validation, commencing with small networks and extending to real-world applications on the IEEE 118-bus test system and the 9241-bus European high-voltage transmission network. In comparison to the integrated network method, our approach has achieved state estimation answers with reduced computation time. The partitioning of the integrated network into multi areas has effectively mitigated computational loads, showcasing its potential for enhancing operational efficiency and reliability in complex power transmission systems. This approach not only offers a robust solution for state estimation but also represents a significant stride toward advancing the field of state estimation, promising to bolster the stability and performance of modern power grids.

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“…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%

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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Multi‐area state estimation in a distribution network using Takagi‐Sugeno model estimated by Kalman filter

Cited by 5 publications

References 33 publications

A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges

A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges

A parallel approach for ultra-fast state estimation in large power system using graph partitioning theory

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

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