.png){kind=icon}
返回
GPU-Accelerated Algorithm for Online Probabilistic Power Flow
.png){kind=icon}
PRE
AI
DOI：10.1109/TPWRS.2017.2756339.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
GPU
probabilistic power flow
Monte-Carlo simulation
simple random sampling
uncertainty source
online
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
机构
查看更多机构
引用论文
.png){kind=icon}
分享收藏分享收藏Cyber physical systems design, methodology, and integration: the current status and future outlook
PREAI
分享收藏 没有更多内容