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基于风力机尾流排斥的平坦地形风电场微观选址优化
作者:
作者单位:

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

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基金项目:

国家自然科学基金资助项目(U186510009);江苏省国际科技合作项目(BZ2018007);江苏省青年基金资助项目(BK20180505)。


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

1.College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China;2.College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;3.Nanjing Heda Wind Power Technology Co., Ltd., Nanjing 211300, China

Fund Project:

This work is supported by National Natural Science Foundation of China (No. U186510009), Jiangsu International Science and Technology Cooperation Project (No. BZ2018007), and Jiangsu Provincial Youth Fund of China (No. BK20180505).

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    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 and stable meta-heuristic algorithm 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 result moreover, while the optimization results meet the actual engineering requirements.

    图1 任意2台风力机的位置关系Fig.1 Position relationship between two wind turbines
    图2 WWRO算法流程图Fig.2 Flow chart of WWRO algorithm
    图3 风能玫瑰图Fig.3 Rose illustration of wind energy
    图4 不同方法的风力机布局结果Fig.4 Wind turbine layout results with different methods
    图5 2种算法的风力机优化布局迭代曲线Fig.5 Iteration curves of layout optimization of wind turbines based on two algorithms
    图 佳点集算法在二维平面内产生100个点的分布图Fig. The good point set algorithm generate a distribution map of 100 points in a two-dimensional plane
    图 风电场地形图Fig. Wind farm topographic map
    表 2 3种排布方法的平均输出功率Table 2 Averagepoweroutput with three layout methods
    表 1 威布尔参数Table 1 Weibull parameters
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引用本文

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/OL].电力系统自动化,http://doi.org/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/OL].Automation of Electric Power Systems,http://doi.org/10.7500/AEPS20191024006.

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  • 收稿日期:2019-10-24
  • 最后修改日期:2020-04-16
  • 录用日期:2020-02-14
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