Cataloged from PDF version of article.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 ...
A coarse-grain parallel implementation is presented of LU factorisation, forward and backward substi...
With the increase of size and complexity of interconnected power system, the dynamic stability simul...
Research is on-going that examines parallel direct block-diagonal-bordered sparse linear solvers for...
Abstract- Standard sparsity-based algorithms used in power system appllcations need to be restructur...
Vector processors have promised an enormous increase in computing speed for computationally intensiv...
Power systems computations for nowadays common large distributed systems typically involve the usage...
This article presents two fast, sparsity-based power system matrices computation procedures. The fir...
AbstractThe fast decoupled load flow (FDLF) program which employing sparse matrix techniques needs l...
Solving sparse systems of linear equations permeates power system analysis. Newton-Raphson, decouple...
AbstractThe power flow computing and optimal power flow computing are the most used tools within the...
This paper presents some recent ideas on and methods created by power engineers for the solution of ...
Solving nonlinear systems of equations is a central problem in numerical analysis, with enormous sig...
AbstractPower flow computing and optimal power flow computing are the most used tools within the pow...
Power flow computation is ubiquitous in the operation and planning of power systems.\ud Traditional ...
With the increase of size and complexity of interconnected power system, the dynamic stability simul...
A coarse-grain parallel implementation is presented of LU factorisation, forward and backward substi...
With the increase of size and complexity of interconnected power system, the dynamic stability simul...
Research is on-going that examines parallel direct block-diagonal-bordered sparse linear solvers for...
Abstract- Standard sparsity-based algorithms used in power system appllcations need to be restructur...
Vector processors have promised an enormous increase in computing speed for computationally intensiv...
Power systems computations for nowadays common large distributed systems typically involve the usage...
This article presents two fast, sparsity-based power system matrices computation procedures. The fir...
AbstractThe fast decoupled load flow (FDLF) program which employing sparse matrix techniques needs l...
Solving sparse systems of linear equations permeates power system analysis. Newton-Raphson, decouple...
AbstractThe power flow computing and optimal power flow computing are the most used tools within the...
This paper presents some recent ideas on and methods created by power engineers for the solution of ...
Solving nonlinear systems of equations is a central problem in numerical analysis, with enormous sig...
AbstractPower flow computing and optimal power flow computing are the most used tools within the pow...
Power flow computation is ubiquitous in the operation and planning of power systems.\ud Traditional ...
With the increase of size and complexity of interconnected power system, the dynamic stability simul...
A coarse-grain parallel implementation is presented of LU factorisation, forward and backward substi...
With the increase of size and complexity of interconnected power system, the dynamic stability simul...
Research is on-going that examines parallel direct block-diagonal-bordered sparse linear solvers for...