Micro-siting Optimization of Wind Farm in Flat Terrain Based on Wind Turbine Wake Repellency

Jie WANG; Chang XU; Xingxing HAN; Ziyang XIN; Feifei XUE; Linmin LI

doi:10.7500/AEPS20191024006

您当前的位置：

首页 >

文章列表页 >

Micro-siting Optimization of Wind Farm in Flat Terrain Based on Wind Turbine Wake Repellency

Basic Research | 更新时间：2025-06-16

- Micro-siting Optimization of Wind Farm in Flat Terrain Based on Wind Turbine Wake Repellency
- Automation of Electric Power Systems Vol. 44, Issue 15, Pages: 62-69(2020)
- 作者机构：
  
  1. 河海大学能源与电气学院，江苏省南京市 211100
  2. 河海大学水利水电学院，江苏省南京市 210098
  3. 南京河大风电科技有限公司，江苏省南京市 211300
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China;Jiangsu International Science and Technology Cooperation Project;Jiangsu Provincial Youth Fund of China
- DOI：10.7500/AEPS20191024006
  CLC：
- Received：24 October 2019，
  
  Revised：2020-02-11，
  
  Published Online：22 April 2020，
  
  Published：10 August 2020
- 稿件说明：
移动端阅览
王洁,许昌,韩星星等.基于风力机尾流排斥的平坦地形风电场微观选址优化[J].电力系统自动化,2020,44(15):62-69.

WANG Jie,XU Chang,HAN Xingxing,et al.Micro-siting Optimization of Wind Farm in Flat Terrain Based on Wind Turbine Wake Repellency[J].Automation of Electric Power Systems,2020,44(15):62-69.
王洁,许昌,韩星星等.基于风力机尾流排斥的平坦地形风电场微观选址优化[J].电力系统自动化,2020,44(15):62-69. DOI： 10.7500/AEPS20191024006.

WANG Jie,XU Chang,HAN Xingxing,et al.Micro-siting Optimization of Wind Farm in Flat Terrain Based on Wind Turbine Wake Repellency[J].Automation of Electric Power Systems,2020,44(15):62-69. DOI： 10.7500/AEPS20191024006.

摘要

平坦地形风电场微观选址的优化研究中，常采用带有随机量的元启发式算法，这些算法难以达到全局最优，优化效率较低，且通常需要通过划分网格、增加约束才能得到稳定解。针对这些问题，文中提出了一种专门针对平坦地形风电场微观选址的高效且有稳定优化解的元启发式算法——风力机尾流排斥优化算法。参考范德华力原理，提出了风力机尾流排斥力的概念，用于描述风力机在尾流作用下的相互影响：2台风力机之间尾流作用越大，风力机尾流排斥力也越大。在算法优化过程中，风力机会在风力机尾流排斥力的作用下向使尾流减小的方向不断移动，直到达到最优解。仿真结果表明：风力机尾流排斥优化算法不需要网格约束，优化效率高，能得到确定的优质解，且优化结果符合实际工程要求。

Abstract

For micro-siting optimization of wind farms in the flat terrain

the meta-heuristic algorithms with random quantities are frequently used. These algorithms are hard to achieve a global optimization

and are relatively inefficient. Grids and constraints are usually needed to make a stable solution. To address these problems

this paper proposes an efficient meta-heuristic algorithm with stable optimization solution for the micro-siting of wind farms in the flat terrain

i.e.

wind turbine wake repellency optimization algorithm. According to the principle of van der Waals force

the concept of wind turbine wake repulsive force is proposed

which is used to describe the mutual influence between wind turbines with wake effects. The greater the wake effect between two wind turbines

the greater the wind turbine repulsive force. During the process of optimization

the wind turbine will continuously move in the direction to reduce the wake under the action of the wind turbine wake repulsive force until the optimal solution is obtained. Simulation results show that the wind turbine wake repellency optimization algorithm does not need grid constraints

has a high optimization efficiency

and can obtain a certain optimal result

while the optimization results meet the actual engineering requirements.

关键词

Keywords

references

GONZÁLEZ J S , PAYÁN M B , SANTOS J R , et al . A review and recent developments in the optimal wind-turbine micro-siting problem [J]. Renewable & Sustainable Energy Reviews , 2014 , 30 ( 2 ): 133 - 144 .

MOSETTI G , POLONI C , DIVIACCO B . Optimization of wind turbine positioning in large wind farms by means of a genetic algorithm [J]. Journal of Wind Engineering & Industrial Aerodynamics , 1994 , 51 ( 1 ): 105 - 116 .

GRADY S A , HUSSAINI M Y , ABDULLAH M M . Placement of wind turbines using genetic algorithms [J]. Renewable Energy , 2014 , 30 ( 2 ): 259 - 270 .

WAN C , WANG J , YANG G , et al . Optimal micro-siting of wind turbines by genetic algorithms based on improved wind and turbine models [C]// Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, December 15-18 , 2009 , Shanghai , China .

严彦 , 许昌 , 刘德有 , 等 . 基于遗传算法的风电场微观选址优化研究 [J]. 太阳能学报 , 2013 , 34 ( 3 ): 526 - 532 .

YAN Yan , XU Chang , LIU Deyou , et al . Optimization research of wind farm micro sitting based on genetic algorithm [J]. Acta Energiae Solaris Sinica , 2013 , 34 ( 3 ): 526 - 532 .

WAN C , WANG J , YANG G , et al . Particle swarm optimization based on Gaussian mutation and its application to wind farm micro-siting [C]// 49th IEEE Conference on Decision and Control, December 15-17 , 2010 , Atlanta , USA .

HUANG W , CHE W X , TAN R S , et al . Wind farm micro-siting optimization using novel cell membrane approach [C]// International Conference on New Energy and Future Energy System, August 19-22 , 2016 , Beijing , China .

REHMAN S , ALI S S , KHAN S A . Wind farm layout design using cuckoo search algorithms [J]. Applied Artificial Intelligence , 2017 , 30 ( 10 ): 899 - 922 .

FORMATO R A . Central force optimization: a new meta heuristic with applications in applied electromagnetics [J]. Progress in Electromagnetics Research , 2007 , 77 : 425 - 491 .

FORMATO R A . Parameter-free deterministic global search with simplified central force optimization [C]// 6th International Conference on Intelligent Computing, August 18-21 , 2010 , Changsha , China .

钱伟懿 , 张桐桐 . 自适应中心引力优化算法 [J]. 计算机科学 , 2012 , 39 ( 6 ): 207 - 209 .

QIAN Weiyi , ZHANG Tongtong . Adaptive central force optimization algorithm [J]. Computer Science , 2012 , 39 ( 6 ): 207 - 209 .

DZYALOSHINSKII I E , LIFSHITZ E M , PITAEVSKII L P . The general theory of van der Waals forces [J]. Advances in Physics , 1961 , 10 ( 38 ): 165 - 209 .

刘杰 , 王宇平 . 种群规模自适应控制的中心引力优化 [J]. 北京邮电大学学报 , 2015 , 38 ( 6 ): 15 - 19 .

LIU Jie , WANG Yuping . Central force optimization with self-adaptive control strategy of population size [J]. Journal of Beijing University of Posts and Telecommunications , 2015 , 38 ( 6 ): 15 - 19 .

华罗庚 , 王元 . 数论在近似分析中的应用 [M]. 北京 : 科学出版社 , 1978 : 22 - 26 .

HUA Luogeng , WANG Yuan . The application of number theory in approximate analysis [M]. Beijing : Science Press , 1978 : 22 - 26 .

肖赤心 , 蔡自兴 , 王勇 , 等 . 一种基于佳点集原理的约束优化进化算法 [J]. 控制与决策 , 2009 , 24 ( 2 ): 249 - 253 .

XIAO Chixin , CAI Zixing , WANG Yong , et al . Constrained optimization evolutionary algorithm based on good lattice points principle [J]. Control and Decision , 2009 , 24 ( 2 ): 249 - 253 .

刘杰 . 全局优化问题的几类新算法 [D]. 西安 : 西安电子科技大学 , 2015 .

LIU Jie . Several new algorithms for global optimization problems [D]. Xi’an : Xidian University , 2015 .

曹娜 , 赵海翔 , 任普春 , 等 . 风电场动态分析中风速模型的建立及应用 [J]. 中国电机工程学报 , 2007 , 27 ( 36 ): 68 - 72 .

CAO Na , ZHAO Haixiang , REN Puchun , et al . Establish and application of wind speed model in wind farm dynamic analysis [J]. Proceedings of the CSEE , 2007 , 27 ( 36 ): 68 - 72 .

许昌 , 钟淋涓 . 风电场规划与设计 [M]. 北京 : 中国水利水电出版社 , 2014 .

XU Chang , ZHONG Linjuan . Wind farm plan and design [M]. Beijing : China Waterpower Press , 2014 .

曾利华 , 王丰 , 刘德有 . 风电场风机尾流及其迭加模型的研究 [J]. 中国电机工程学报 , 2011 , 31 ( 19 ): 37 - 42 .

ZENG Lihua , WANG Feng , LIU Deyou . Research on wind turbine wake model and overlapping in wind farm [J]. Proceedings of the CSEE , 2011 , 31 ( 19 ): 37 - 42 .

许昌 , 杨建川 , 李辰奇 , 等 . 复杂地形风电场微观选址优化 [J]. 中国电机工程学报 , 2013 , 33 ( 31 ): 58 - 64 .

XU Chang , YANG Jianchuan , LI Chenqi , et al . Optimization of wind farm layout in complex terrain [J]. Proceedings of the CSEE , 2013 , 33 ( 31 ): 58 - 64 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Capacity Optimal Configuration of Distributed Offshore Wind Power to Hydrogen Considering Micro-siting

风电经柔直送出系统送端交流故障低电压穿越策略

基于单机模型扩展的直驱风电场通用等值模型构建方法

基于虚拟调速控制的大型孤岛新能源基地LCC直流送出方案

Day-ahead Topology Optimization for Wind Farm Without Forecasting Based on Improved Deep Q Network

Related Author

DU Yunfei

SHEN Xinwei

LI Hongke

LI Jiarong

LU Boan

ZHONG Shuwei

CHE Yanbo

SU Jianbo

Related Institution

Tsinghua Shenzhen International Graduate School

PowerChina Huadong Engineering Corporation Limited

Department of Electrical Engineering, Tsinghua University

College of Electrical and Information Engineering, Hunan University

⁰