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Journal ArticleDOI

Sparse Vector Methods

W. F. Tinney, +2 more
- 01 Feb 1985 - 
- Vol. 104, Iss: 2, pp 295-301
TLDR
The efficiency of the sparse vector methods is verified by tests on a 156-bus, a 1598-bus and a 2265-bus system and the new methods are significantly faster than the established sparse matrix techniques.
Abstract
Sparse vector methods enhance the efficiency of matrix solution algorithms by exploiting the sparsity of the independent vector and/or the desire to know only a subset of the unknown vector. This paper shows how these methods can be efficiently implemented for sparse matrices. The efficiency of the sparse vector methods is verified by tests on a 156-bus, a 1598-bus and a 2265-bus system. In all cases tested, the new methods are significantly faster than the established sparse matrix techniques.

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References
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Journal ArticleDOI

Optimal Power Flow By Newton Approach

TL;DR: In this paper, a direct simultaneous solution for all of the unknowns in the Lagrangian function on each iteration is proposed, where each iteration minimizes a quadratic approximation of the Lagrangeian.
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Sparsity-Oriented Compensation Methods for Modified Network Solutions

TL;DR: The paper gives a unified derivation and analysis of compensation methods for the efficient solution of network problems involving matrix modifications, including the removal, addition and splitting of tiodes.
Journal ArticleDOI

Generalized Method of Fault Analysis

TL;DR: A generalized method is given for solving shortcircuit faults of any conceivable complexity that efficiently combines the application of sparsity-oriented compensation techniques to sequence networks with the simulation of fault conditions in phase coordinates.
Trending Questions (1)
Which sparse matrix representations are most efficient for linear algebra computations?

In all cases tested, the new methods are significantly faster than the established sparse matrix techniques.