风电场尾流控制中偏航角优化算法研究

丛龙福; 戴丽萍; 常宁; 邹昂; 周亦潇; 王子坤

doi:doi:10.19912/j.0254-0096.tynxb.2024-1295

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风电场尾流控制中偏航角优化算法研究

更新时间：2025-12-10

- 风电场尾流控制中偏航角优化算法研究
- Issue 11, Pages: 747-753(2025)
- 作者机构：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1295
  CLC： K89
- Published Online：04 December 2025，
  
  Published：04 December 2025
- 稿件说明：
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丛龙福, 戴丽萍, 常宁, et al. 风电场尾流控制中偏航角优化算法研究[J]. 2025, (11): 747-753.
DOI：

丛龙福, 戴丽萍, 常宁, et al. 风电场尾流控制中偏航角优化算法研究[J]. 2025, (11): 747-753. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1295.

摘要

首先基于Ishihara高斯尾流模型、能量守恒叠加尾流模型、秃鹫等多种优化算法建立精确的偏航角计算流程并比较各算法的效率

通过与文献中的大涡模拟（LES）数据对比分析湍流叠加系数对结果的影响。随后

基于所提出的3步快速计算（TSFC）优化算法建立偏航角快速优化流程。分别采用上述两种优化算法对串列风力机和优化布局风力机进行偏航角优化计算。结果表明

当湍流叠加系数为2.8时

下游湍流度预测较为准确;对不同布局的风场

采用主动偏航功率可提高0.4%~2.2%。此外

TSFC算法在效率上显著优于秃鹫优化算法

运行时间减少一个数量级。

Abstract

This paper establishes an accurate process for yaw angle calculation based on the Ishihara Gaussian wake model

the energy-conserving wake superposition model

and several optimization algorithms

including the African Vulture Optimization Algorithm （AVOA）. The efficiency of these algorithms is evaluated

and the influence of the turbulence superposition coefficient on the results is analyzed by comparing with LES data from the literature. Furthermore

a rapid yaw angle optimization process is developed using the TSFC optimization algorithm proposed in this study. Both optimization algorithms are applied to yaw angle optimization for turbines in tandem and in optimized layout configurations. The results indicate that a turbulence superposition coefficient of 2.8 leads to more accurate predictions of downstream turbulence intensity. For wind farms with varying layouts

active yaw control can improve power generation by 0.4% to 2.2%. Additionally

the TSFC algorithm significantly outperforms the African Vulture Optimization Algorithm in terms of efficiency

reducing computational time by an order of magnitude.

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references

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