Optimal placement of wind turbines within a wind farm considering multi-directional wind speed using two-stage genetic algorithm

被引:0
作者
A. S. O. Ogunjuyigbe
T. R. Ayodele
O. D. Bamgboje
机构
[1] University of Ibadan,Power Energy Machines and Drives (PEMD) Research Group, Department of Electrical and Electronic Engineering
来源
Frontiers in Energy | 2021年 / 15卷
关键词
optimal placement; wind turbines; wind direction; genetic algorithm; wake effect;
D O I
暂无
中图分类号
学科分类号
摘要
Most wind turbines within wind farms are set up to face a pre-determined wind direction. However, wind directions are intermittent in nature, leading to less electricity production capacity. This paper proposes an algorithm to solve the wind farm layout optimization problem considering multi-angular (MA) wind direction with the aim of maximizing the total power generated on wind farms and minimizing the cost of installation. A twostage genetic algorithm (GA) equipped with complementary sampling and uniform crossover is used to evolve a MA layout that will yield optimal output regardless of the wind direction. In the first stage, the optimal wind turbine layouts for 8 different major wind directions were determined while the second stage allows each of the previously determined layouts to compete and inter-breed so as to evolve an optimal MA wind farm layout. The proposed MA wind farm layout is thereafter compared to other layouts whose turbines have focused site specific wind turbine orientation. The results reveal that the proposed wind farm layout improves wind power production capacity with minimum cost of installation compared to the layouts with site specific wind turbine layouts. This paper will find application at the planning stage of wind farm.
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页码:240 / 255
页数:15
相关论文
共 52 条
[1]  
Ayodele T R(2015)Increasing household solar energy penetration through load partitioning based on quality of life: the case study of Nigeria Sustainable Cities and Society 18 21-31
[2]  
Ogunjuyigbe A S O(2014)Viability and economic analysis of wind energy resource for power generation in Johannesburg, South Africa International Journal of Sustainable Energy 33 284-303
[3]  
Ayodele T R(2016)Wind energy potential of vesleskarvet and the feasibility of meeting the South African’s SANAE IV energy demand Renewable & Sustainable Energy Reviews 56 226-234
[4]  
Jimoh A A(2002)On some of the design aspects of wind energy conversion systems Energy Conversion and Management 43 2175-2187
[5]  
Munda J L(2002)A viscous three-dimensional differential/actuator-disk method for the aerodynamic analysis of wind farms Solar Energy Engineering 124 345-356
[6]  
Agee J T(2011)Optimization of wind turbine placement using a viral based optimization algorithm Procedia Computer Science 6 469-474
[7]  
Ayodele T R(2008)Optimal placement of wind turbines in a wind park using monte carlo simulation Renewable Energy 33 1455-1460
[8]  
Ogunjuyigbe A S O(2014)A genetic algorithm analysis towards optimization solutions International Journal of Digital Information and Wireless Communications 4 124-142
[9]  
Bansal R C(1994)Optimization of wind turbine positioning in large wind farms by means of a genetic algorithm Journal of Wind Engineering and Industrial Aerodynamics 51 105-116
[10]  
Bhatti T S(2005)Placement of wind turbines using genetic algorithms Renewable Energy 30 259-270