Fast decoupled load flow: hypothesis, derivations, and testing

Monticelli, A.; Garcia, A.V.; Saavedra, Osvaldo R.

doi:10.1109/59.99396

Cited by 123 publications

(44 citation statements)

References 4 publications

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“…Traditionally, the power flow problem is solved using the Newton-Raphson method with a direct solver [8], [9], or using the fast decoupled load flow (FDLF) method [10]- [12]. In [6] we showed that the LU factorization-which is used by both these traditional methods-is not viable for very large power flow problems.…”

Section: The Power Flow Problemmentioning

confidence: 99%

Towards Faster Solution of Large Power Flow Problems

Idema

Papaefthymiou

Lahaye

et al. 2013

IEEE Trans. Power Syst.

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Section: The Power Flow Problemmentioning

confidence: 99%

Towards Faster Solution of Large Power Flow Problems

Idema

Papaefthymiou

Lahaye

et al. 2013

IEEE Trans. Power Syst.

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“…The FDLF matrix Φ can be seen as an approximate Schur complement of the initial Jacobian matrix [15]. In previous studies Φ has already proven to be a good preconditioner [10], [12], while containing only half the non-zeros of the Jacobian matrix, thus providing benefits in computing time and memory.…”

Section: Preconditioningmentioning

confidence: 99%

Scalable Newton-Krylov Solver for Very Large Power Flow Problems

Idema

Lahaye

Vuik

et al. 2012

IEEE Trans. Power Syst.

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“…Thus, in references [3][4][5][6], efficient load flow algorithms are presented, which are based on fast decoupled load flow. However, since these methods always use fast converging load flow algorithms, the convergence is not guaranteed in heavily loaded systems.…”

Section: Introductionmentioning

confidence: 99%

Contingency evaluation and monitorization using artificial neural networks

Joya

Garcìa-Lagos

Hernández

2009

Neural Comput & Applic

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In this paper, different neural network-based solutions to the contingency analysis problem are presented. Contingency analysis is examined from two perspectives: as a functional approximation problem obtaining a numerical evaluation and ranking contingencies; and as a graphical monitoring problem, obtaining an easy visualization system of the relative severity of the contingencies. For the functional evaluation problem, we analyze the use of different supervised feed-forward artificial neural networks (multilayer perceptron and radial basis function networks). The proposed systems produce a very accurate evaluation and ranking, and so present a high applicability. For the graphical monitoring problem, unsupervised artificial neural networks such as self-organizing maps by Kohonen have been used. This solution allows both a rapid, easy and simultaneous visualization of the severity level of the complete contingency set. The proposed solutions avoid the main drawbacks of previous neural network approaches to this problem, which are explicitly analyzed here.

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Fast decoupled load flow: hypothesis, derivations, and testing

Cited by 123 publications

References 4 publications

Towards Faster Solution of Large Power Flow Problems

Towards Faster Solution of Large Power Flow Problems

Scalable Newton-Krylov Solver for Very Large Power Flow Problems

Contingency evaluation and monitorization using artificial neural networks

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