GPU-Accelerated Algorithm for On-Line Probabilistic Power Flow
This letter proposes a superior GPU-accelerated algorithm for probabilistic power flow (PPF) based on Monte-Carlo simulation with simple random sampling (MCS-SRS). By means of offloading the tremendous computational burden to GPU, the algorithm can solve PPF in an extremely fast manner, two orders of magnitude faster in comparison to its CPU-based counterpart. Case studies on three large-scale systems show that the proposed algorithm can solve a whole PPF analysis with 10000 SRS and ultra-high-dimensional dependent uncertainty sources in seconds and therefore presents a highly promising solution for online PPF applications.
G. Zhou et al., "GPU-Accelerated Algorithm for On-Line Probabilistic Power Flow," IEEE Transactions on Power Systems, vol. 33, no. 1, pp. 1132-1135, Institute of Electrical and Electronics Engineers (IEEE), Jan 2018.
The definitive version is available at https://doi.org/10.1109/TPWRS.2017.2756339
Electrical and Computer Engineering
Keywords and Phrases
Graphics Processing Unit; Intelligent Systems; Large Scale Systems; Monte Carlo Methods; Online Systems; Uncertainty Analysis; Case-Studies; Computational Burden; GPU-Accelerated; Online; Orders of Magnitude; Probabilistic Power Flow; Simple Random Sampling; Uncertainty Sources; Electric Load Flow; GPU; Monte-Carlo Simulation; Uncertainty Source
International Standard Serial Number (ISSN)
Article - Journal
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