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Journal ArticleDOI

A Decoupled Orthogonal Row Processing Algorithm for Power System State Estimation

J. W. Wang, +1 more
- 01 Aug 1984 - 
- Vol. 103, Iss: 8, pp 2337-2344
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TLDR
This paper presents a fast, reliable and storage -saving algorithm for Power System State Estimation (PSSE) that employs a Single sub-Matrix of a Decoupled Jacobian matrix (SMDJ) to solve a PSSE weighted least-squares problem.
Abstract
This paper presents a fast, reliable and storage -saving algorithm for Power System State Estimation (PSSE). Instead of using a Single sub-Matrix of a Decoupled Gain matrix (SMDG), the new algorithm employs a Single sub-Matrix of a Decoupled Jacobian matrix (SMDJ); the Givens transformations is then used to solve a PSSE weighted least-squares problem. Thus, the algorithm has the advantages of both decoupling and orthogonal transformations. It will be shown in theory and practice that the new algorithm performs better than the algorithm using the SMDG. The new algorithm has been tested on two power systems, including an IEEE 30-bus test power system. From the numerical results we conclude that the proposed algorithm is considerably superior to the conventional normal equation algorithm and other decoupling algorithms.

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Citations
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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.
Journal ArticleDOI

Comparison of different methods for state estimation

TL;DR: In this paper, a comparative study of five methods, namely, the normal equations method, the orthogonal transformation method, hybrid method, normal equations with constraints, and Hachtel's augmented matrix method for state estimation has been conducted.
Journal ArticleDOI

Real-time modeling of power networks

TL;DR: The various steps in constructing the model from the real-time measurements are described, including the determination of the network topology, the estimation of thenetwork state, and the approximate modeling of the unobservable (external) network.
Journal ArticleDOI

State estimation for distribution systems with zero-injection constraints

TL;DR: In this paper, a fast decoupled state estimator with equality constraints is proposed for three-phase distribution systems, which uses a compact constant-symmetric gain matrix which can be decomposed into two "identical" sub-gain matrices.
Journal ArticleDOI

Observability analysis and bad data processing for state estimation with equality constraints

TL;DR: In this article, a factorization-based observability analysis and normalized residual-based bad-data processing are extended to state estimation with equality constraints, and the normalized residuals are calculated using the sparse inverse of the gain matrix.
References
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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

Bad data analysis for power system state estimation

TL;DR: In this article, the state estimation problem in electric power systems consists of four basic operations: hypothesize structure; estimate; detect; identify, which is addressed with respect to the bad data and structural error problem.
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.
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