GPU-Based Fast Decoupled Power Flow With Preconditioned Iterative Solver and Inexact Newton Method

Power flow is the most fundamental computation in power system analysis. Traditionally, the linear solution in power flow is solved by a direct method like LU decomposition on a CPU platform. However, the serial nature of the LU-based direct method is the main obstacle for parallelization and scalab...

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Bibliographic Details
Published in:IEEE transactions on power systems Vol. 32; no. 4; pp. 2695 - 2703
Main Authors: Li, Xue, Li, Fangxing, Yuan, Haoyu, Cui, Hantao, Hu, Qinran
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Architecture
Buses (vehicles)
Computer architecture
conjugate gradient (CG) method
Convergence
Fast decoupled power flow (FDPF)
graphic processing unit (GPU)
Graphics processing units
inexact newton method
Iterative methods
iterative solver
Load flow
Matlab
Newton method
Nonlinear programming
Parallel processing
Power flow
precondition
Solvers
ISSN:0885-8950, 1558-0679
Online Access:Get full text
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Summary:Power flow is the most fundamental computation in power system analysis. Traditionally, the linear solution in power flow is solved by a direct method like LU decomposition on a CPU platform. However, the serial nature of the LU-based direct method is the main obstacle for parallelization and scalability. In contrast, iterative solvers, as alternatives to direct solvers, are generally more scalable with better parallelism. This study presents a fast decouple power flow (FDPF) algorithm with a graphic processing unit (GPU)-based preconditioned conjugate gradient iterative solver. In addition, the Inexact Newton method is integrated to further improve the GPU-based parallel computing performance for solving FDPF. The results show that the GPU-based FDPF maintains the same precision and convergence as the original CPU-based FDPF, while providing considerable performance improvement for several large-scale systems. The proposed GPU-based FDPF with the Inexact Newton method gives a speedup of 2.86 times for a system with over 10 000 buses if compared with traditional FDPF, both implemented based on MATLAB. This demonstrates the promising potential of the proposed FDPF computation using a preconditioned iterative solver under GPU architecture.
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2016.2618889