Effects of environmental and turbine parameters on energy gains from wind farm system: Artificial neural network simulations

被引：9

作者：

Abidoye, Luqman K. ^{[1
]}

Bani-Hani, Ehab ^{[2
]}

Assad, Mamdouh El Haj ^{[3
]}

AlShabi, Mohammad ^{[4
]}

Soudan, Bassel ^{[5
]}

Oriaje, Aremu T. ^{[1
]}

机构：

[1] Osun State Univ, Fac Engn, Osogbo, Nigeria

[2] Australian Coll Kuwait, Dept Mech Engn, Kuwait, Kuwait

[3] Univ Sharjah, Dept Sustainable & Renewable Energy Engn SREE, POB 27272, Sharjah, U Arab Emirates

[4] Univ Sharjah, Dept Mech Engn, Sharjah, U Arab Emirates

[5] Univ Sharjah, Dept Elect & Comp Engn, Sharjah, U Arab Emirates

来源：

WIND ENGINEERING | 2020年 / 44卷 / 02期

关键词：

Artificial neural network; wind farm; wind turbine; energy gains; rotor diameter; 2-PHASE FLOW; STATE; MODEL;

D O I：

10.1177/0309524X19849834

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Artificial neural network modelling has been employed to investigate the effects of various environmental and machine factors on the energy gain from wind farm systems. Numerical comparison of artificial neural network and nonlinear regression from XLSTAT showed that ANN possessed better numerical accuracy in predicting multivariate data. Several artificial neural network models are developed and tested with several structures to obtain the best prediction performance in energy gain from different wind farms in Jordan. The best performing artificial neural network model was used to predict the energy gain from wind farm based on changes in annual wind speed, turbine rotor diameter and turbine power. As a result of 20% increase in turbine power, 14.4%-31% energy gains were recorded across different wind farms. The proposed artificial neural network model was also a good predictor for energy cost resulting from specific wind farm design.

引用

页码：181 / 195

页数：15

共 26 条

[1] Simple dielectric mixing model in the monitoring of CO2 leakage from geological storage aquifer [J].