GPU-based power flow analysis with Chebyshev preconditioner and conjugate gradient method

被引：41

作者：

Li, Xue ^{[1
]}

Li, Fangxing ^{[1
]}

机构：

[1] Univ Tennessee, Dept EECS, Knoxville, TN 37996 USA

来源：

ELECTRIC POWER SYSTEMS RESEARCH | 2014年 / 116卷

基金：

美国国家科学基金会;

关键词：

Power flow; Parallel processing; CUDA; Graphic processing unit (CPU); Chebyshev preconditioner; Conjugate gradient method; SOLVER; SYSTEM;

D O I：

10.1016/j.epsr.2014.05.005

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Traditionally, linear equations in power system applications are solved by direct methods based on LU decomposition. With the development of advanced power system controls, the industrial and research community is more interested in simulating larger, interconnected power grids. Iterative methods such as the conjugate gradient method have been applied to power system applications in the literature for its parallelism potential with larger systems. Preconditioner, used for preconditioning the linear system for a better convergence rate in iterative computations, is an indispensable part of iterative solving process. This work implemented a polynomial preconditioner Chebyshev preconditioner with graphic processing unit (GPU), and integrated a GPU-based conjugate gradient solver. Results show that GPU-based Chebyshev preconditioner can reach around 46x speedup for the largest test system, and conjugate gradient can gain more than 4x speedup. This demonstrates great potentials for CPU application in power system simulation. (C) 2014 Published by Elsevier B.V.

引用

页码：87 / 93

页数：7

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