GPU-OpenCL accelerated probabilistic power flow analysis using Monte-Carlo simulation

被引:26
作者
Abdelaziz, Morad [1 ]
机构
[1] Univ Laval, Dept Elect & Comp Engn, Quebec City, PQ, Canada
关键词
GPU; Monte-Carlo simulation; Probabilistic power flow; Renewable energy; WIND SPEEDS; SYSTEMS;
D O I
10.1016/j.epsr.2017.02.022
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper investigates the acceleration of Monte-Carlo (MC) based probabilistic power flow (PPF) analysis by exploiting the massively parallel architecture of graphics processing units (GPUs). (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:70 / 72
页数:3
相关论文
共 16 条
[11]  
Roberge V., 2017, IEEE T SMAR IN PRESS
[12]   Effect of load models in distributed generation planning [J].
Singh, Devender ;
Misra, R. K. ;
Singh, Deependra .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2007, 22 (04) :2204-2212
[13]  
Su CL, 2012, 2012 IEEE ASIA PACIFIC CONFERENCE ON CIRCUITS AND SYSTEMS (APCCAS), P448, DOI 10.1109/APCCAS.2012.6419068
[14]   Probabilistic load-flow computation using point estimate method [J].
Su, CL .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2005, 20 (04) :1843-1851
[15]  
Wenyuan L., 1991, J IEEE T SYSTEMS, V6, P1522, DOI [10.1109/59.116999, DOI 10.1109/59.116999]
[16]   Parallel Massive-Thread Electromagnetic Transient Simulation on GPU [J].
Zhou, Zhiyin ;
Dinavahi, Venkata .
IEEE TRANSACTIONS ON POWER DELIVERY, 2014, 29 (03) :1045-1053