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Journal ArticleDOI

State Estimation in Power Systems Part I: Theory and Feasibility

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TLDR
State estimation is a digital processing scheme which provides a real-time data base for many of the central control and dispatch functions in a power system as discussed by the authors, where the estimator processes the imperfect information available and produces the best possible estimate of the true state of the system.
Abstract
State estimation is a digital processing scheme which provides a real-time data base for many of the central control and dispatch functions in a power system. The estimator processes the imperfect information available and produces the best possible estimate of the true state of the system. The basic theory and computational requirements of static state estimation are presented, and their impact on the evolution of the data-acquisition, data- processing, and control subsystems are discussed. The feasibility of this technique is demonstrated on network examples.

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Journal ArticleDOI

A Dynamic Estimator for Tracking the State of a Power System

TL;DR: In this paper, the problem of real-time estimation of the state of a power system is treated from the point of view of the theory of least-squares estimation (Kalman-Bucy filtering).
Journal ArticleDOI

Static state estimation in electric power systems

TL;DR: A static state estimator is a collection of digital computer programs which convert telemetered data into a reliable estimate of the transmission network structure and state by accounting for small random metering-communication errors and the need for real-time solutions using limited computer time and storage.
Journal ArticleDOI

Bad Data Suppression in Power System Static State Estimation

TL;DR: In this paper, the authors proposed a bad data suppression (BDS) estimator which is based on a non-quadratic cost function but which reduces to the weighted least squares estimator in the absence of bad data.
Journal ArticleDOI

Power system state estimation: a survey

TL;DR: Concepts of decoupling, ill-conditioning and robustness in state estimation are discussed and derivations ofDecoupled estimators, stable estimators and robust estimators are reviwed.
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State estimation for power distribution system and measurement impacts

Ke Li
TL;DR: A distribution state estimator (DSE) based on the weighted-least-square approach and three-phase modeling techniques is presented in this paper, where the influence of measurement placement and measurement accuracy on the estimated results are discussed.
References
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Journal ArticleDOI

New Results in Linear Filtering and Prediction Theory

TL;DR: The Duality Principle relating stochastic estimation and deterministic control problems plays an important role in the proof of theoretical results and properties of the variance equation are of great interest in the theory of adaptive systems.
Journal ArticleDOI

Power System Static-State Estimation, Part I: Exact Model

TL;DR: Discussions center on the general nature of the problem, mathematical modeling, an interative technique for calculating the state estimate, and concepts underlying the detection and identification of modeling errors.
Journal ArticleDOI

Power Flow Solution by Newton's Method

TL;DR: The ac power flow problem can be solved efficiently by Newton's method because only five iterations, each equivalent to about seven of the widely used Gauss-Seidel method are required for an exact solution.
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Direct solutions of sparse network equations by optimally ordered triangular factorization

TL;DR: With this method, direct solutions are computed from sparse matrix factors instead of from a full inverse matrix, thereby gaining a significant advantage in speed, computer memory requirements, and reduced round-off error.
Journal ArticleDOI

Power System Static-State Estimation, Part II: Approximate Model

TL;DR: An approximate mathematical model related to the dc load-flow model yields noniterative-state estimation equations, simplified prediction of effects of network and generation-load pattern changes on network flow, and simplified detection and identification of modeling errors.
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