Algorithms for efficient vectorization of repeated sparse power system network computations
IEEE Transactions on Power Systems
448 - 456
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Standard sparsity-based algorithms used in power system appllcations need to be restructured for efficient vectorization due to the extremely short vectors processed. Further, intrinsic architectural features of vector computers such as chaining and sectioning should also be exploited for utmost performance. This paper presents novel data storage schemes and vectorization alsorim that resolve the recurrence problem, exploit chaining and minimize the number of indirect element selections in the repeated solution of sparse linear system of equations widely encountered in various power system problems. The proposed schemes are also applied and experimented for the vectorization of power mismatch calculations arising in the solution phase of FDLF which involves typical repeated sparse power network computations. The relative performances of the proposed and existing vectorization schemes are evaluated, both theoretically and experimentally on IBM 3090ArF.
Data Storage Equipment
Electric Load Flow
Fortran (Programming Language)
Parallel Processing Systems
Pipeline Processing Systems
Vectors Efficient Vectorization
Fast Decoupled Load Flow
Sparse Linear System
Sparse Power System Network
Electric Power Systems
Permalink (Please cite this version)http://hdl.handle.net/11693/10801
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