Full Parallel Power Flow Solution: A GPU-CPU-Based Vectorization Parallelization and Sparse Techniques for Newton–Raphson Implementation-Reference-Cited by-同舟云学术

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Full Parallel Power Flow Solution: A GPU-CPU-Based Vectorization Parallelization and Sparse Techniques for Newton–Raphson Implementation

Published:2020-05 Issue:3 Volume:11 Page:1833-1844
ISSN:1949-3053
Container-title:IEEE Transactions on Smart Grid
language:
Short-container-title:IEEE Trans. Smart Grid

Author:

Su Xueneng^ORCID,He Chuan,Liu Tianqi,Wu Lei^ORCID

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/5165411/9075135/08848455.pdf?arnumber=8848455

Cited by 26 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

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2. High Throughput Lattice-Based Signatures on GPUs: Comparing Falcon and Mitaka;IEEE Transactions on Parallel and Distributed Systems;2024-04

3. GPU-resident sparse direct linear solvers for alternating current optimal power flow analysis;International Journal of Electrical Power & Energy Systems;2024-01

4. Risk and vulnerability assessment in power systems;Future Modern Distribution Networks Resilience;2024

5. Analyzing the Implementation of the Newton Raphson Based Power Flow Formulation in CPU+GPU Computing Environment;2023 North American Power Symposium (NAPS);2023-10-15

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