Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 32, Issue 4, Pages 2695-2703Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2016.2618889
Keywords
Fast decoupled power flow (FDPF); graphic processing unit (GPU); inexact newton method; iterative solver; precondition; conjugate gradient (CG) method
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Funding
- NSF [ECCS-1128381]
- National Natural Science Foundation of China [51607033, 51607034]
- CURENT, an Engineering Research Center Program of NSF
- DOE under NSF [EEC-1041877]
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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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