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Fast load flow algorithms

Authors: Jalali-Kushki, Hossein;

Fast load flow algorithms

Abstract

New, fast and reliable algorithms for solving load-flow problems are presented in this thesis. Each of these algorithms iteratively solves a set of linear equations in terms of voltage magnitude squared and phase angles, and converges onto the final solution in a few iterations. Although the line losses of the system are used in deriving the equations of the basic line-loss load-flow algorithm, knowledge of their (approximate) values is not a prerequisite to using the algorithm. The basic line-loss load-flow algorithm is slightly modified to give an incremental-change line-loss algorithm which proves to be always preferable to the basic algorithm. By exploiting the weak interdependence between active power and voltage magnitude, and between reactive power and phase angle, two decoupled versions of the incremental-change line-loss algorithm were also developed. All these algorithms have constant gradient characteristics, and their storage requirements are, at most, the same as those of the standard Newton-Raphson algorithm. If need be, the storage requirements can be reduced to those of the triangularized Y-matrix iterative algorithms. Tests on various systems indicate fast and reliable convergence characteristics better than those of the Newton-Raphson algorithm and comparable, to those obtained by Stott and Alsac with their decoupled Newton-Raphson load-flow algorithm.

Countries
Canada, Canada, United States, Mexico
Keywords

Electric power systems, 518

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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