GPU-based Batch LU-Factorization Solver for Concurrent Analysis of Massive Power Flows
In many power system applications, such as N-x static security analysis and Monte-Carlo-simulation-based probabilistic power flow (PF) analysis, it is a very time-consuming task to analyze massive number of PFs on identical or similar network topology. This letter presents a novel GPU-accelerated batch LU-factorization solver that achieves higher level of parallelism and better memory-access efficiency through packaging massive number of LU-factorization tasks to formulate a new larger-scale problem. The proposed solver can achieve up to 76 times speedup when compared to KLU library and lays a critical foundation for massive-PFs-solving applications.
G. Zhou et al., "GPU-based Batch LU-Factorization Solver for Concurrent Analysis of Massive Power Flows," IEEE Transactions on Power Systems, vol. 32, no. 6, pp. 4975-4977, Institute of Electrical and Electronics Engineers (IEEE), Nov 2017.
The definitive version is available at https://doi.org/10.1109/TPWRS..2662322
Electrical and Computer Engineering
Keywords and Phrases
Computer Graphics; Computer Graphics Equipment; Electric Load Flow; Electric Power Systems; Factorization; Intelligent Systems; Monte Carlo Methods; Parallel Processing Systems; Program Processors; Degree of Parallelism; LU Factorization; Network Topology; Power Flows; Power System Applications; Probabilistic Power Flow; Static Security Analysis; Time-Consuming Tasks; Graphics Processing Unit; Graphics Processing Unit (GPU); Parallel Computing; Power Flow
International Standard Serial Number (ISSN)
Article - Journal
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