高效风能捕获的高空风电优化运行控制策略

付炳喆; 王玮; 罗必雄; 任宗栋; 李沂洹; 杨卧龙

doi:doi:10.19912/j.0254-0096.tynxb.2024-0965

欢迎来到能源电力期刊网！

English Version 读者登录期刊登录

您当前的位置：

首页 >

文章列表页 >

高效风能捕获的高空风电优化运行控制策略

更新时间：2025-12-10

- 高效风能捕获的高空风电优化运行控制策略
- 高效风能捕获的高空风电优化运行控制策略
- 太阳能学报 2025年第11期页码：581-589
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0965
  中图分类号： M614
- 网络出版：2025-12-04，
  
  纸质出版：2025-12-04
- 稿件说明：
移动端阅览
付炳喆, 王玮, 罗必雄, 等. 高效风能捕获的高空风电优化运行控制策略[J]. 太阳能学报, 2025,(11):581-589.

付炳喆, 王玮, 罗必雄, et al. 高效风能捕获的高空风电优化运行控制策略[J]. 2025, (11): 581-589.
付炳喆, 王玮, 罗必雄, 等. 高效风能捕获的高空风电优化运行控制策略[J]. 太阳能学报, 2025,(11):581-589. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0965.

付炳喆, 王玮, 罗必雄, et al. 高效风能捕获的高空风电优化运行控制策略[J]. 2025, (11): 581-589. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0965.

摘要

为实现适应风切变动态变化的高空风力发电系统（AWEs）运行优化控制

达成高效风能捕获

提出一种基于混合高斯过程（MGP）的自适应模型预测控制（MPC）策略。首先

对AWEs运行的关键因素风切变定量分析

使用混合高斯过程对风切变剖面的重要统计特征建模

为高度优化控制提供先验风速信息;然后

建立融合遗传算法的自适应MPC策略以实现运行最优高度和能量损耗之间的平衡

在保证AWE系统运行在理想高度的同时减少控制所消耗的能量

提升AWEs的净发电量;最后

基于中国某高空风能发电示范工程的真实风切变数据验证所提出优化运行控制策略。结果表明

该策略可有效提升AWEs净发电量

较传统固定塔架结构风电机组平均提升18.9%

较极值搜索策略提升9.6%。

Abstract

To achieve the optimization control of airborne wind energy systems （AWEs） that adapt to the dynamic changes of wind shear

and to accomplish efficient wind energy capture

a model predictive control （MPC） strategy based on mixed Gaussian process（MGP） is proposed. Initially

a quantitative analysis of the key factor of AWEs operation

wind shear

is conducted. MGP is utilized to model the significant statistical characteristics of the wind shear profile

providing prior wind speed information for height-optimized control. Subsequently

an adaptive MPC strategy integrated with a genetic algorithm is established to balance the optimal operation height and energy dissipation

ensuring that AWEs operates at the ideal height while reducing the energy consumed for control

thereby enhancing the net power generation of AWEs. Finally

the proposed optimization control strategy is validated based on the real wind shear data from a high-altitude wind energy generation demonstration project in China. The results indicate that the strategy can effectively increase the net power generation of AWEs

with an average increase of 18.9% compared to traditional fixed tower structures and 9.6% compared to the extremum seeking strategy.

关键词

Keywords

references

刘玉山, 王灵梅, 孟恩隆, 等. 基于实测数据的风电机组动态调节性能研究[J]. 电网技术, 2020, 44(11): 4328-4341.

WAN J, REN G R, LIU J F, et al.Ultra-short-term wind speed prediction based on multi-scale predictability analysis[J]. Cluster computing, 2016, 19(2): 741-755.

BECHTLE P, SCHELBERGEN M, SCHMEHL R, et al.Airborne wind energy resource analysis[J]. Renewable energy, 2019, 141: 1103-1116.

和月磊. 直驱式永磁电机在高空风力发电中的应用研究[D]. 哈尔滨: 哈尔滨工业大学, 2011.

LI Q W, WANG J Z, ZHANG H P.Comparison of the goodness-of-fit of intelligent-optimized wind speed distributions and calculation in high-altitude wind-energy potential assessment[J]. Energy conversion and management, 2021, 247: 114737.

CHERUBINI A, PAPINI A, VERTECHY R, et al.Airborne wind energy systems: a review of the technologies[J]. Renewable and sustainable energy reviews, 2015, 51: 1461-1476.

FAGIANO L, QUACK M, BAUER F, et al.Autonomous airborne wind energy systems: accomplishments and challenges[J]. Annual review of control, robotics, and autonomous systems, 2022, 5: 603-631.

LEINEWEBERD B, ARES ZENTRUM I.Analyse und Restrukturierung eines Verfahrens zur direkten L osung von Optimal-Steuerungsproblemen (The Theory of MUSCOD in a Nutshell) [D]. Baden-Württemberg: University of Heidelberg, 1995.

HAN S Q, PANG Y, XIA H.Modeling and tracking control of tethered kite systems[C]//2018 37th Chinese Control Conference (CCC). Wuhan, China, 2018: 7476-7481.

TREVISI F, GAUNAA M, MCWILLIAM M.Unified engineering models for the performance and cost of ground-gen and fly-gen crosswind airborne wind energy systems[J]. Renewable energy, 2020, 162: 893-907.

VERMILLION C.Altitude and crosswind motion control for optimal power-point tracking in tethered wind energy systems with airborne power generation[C]//ASME 2013 Dynamic Systems and Control Conference, October 21-23, 2013, Palo Alto, California, USA. 2014

VERMILLION C, FAGIANO L.Electricity in the air: tethered wind energy systems[J]. Mechanical engineering, 2013, 135(9): 13-21.

BAFANDEH A, VERMILLION C.Altitude optimization of airborne wind energy systems via switched extremum seeking: design, analysis, and economic assessment[J]. IEEE transactions on control systems technology, 2017, 25(6): 2022-2033.

BIN-KARIM S, BAFANDEH A, VERMILLION C.Spatio-temporal optimization through model predictive control: a case study in airborne wind energy[C]//2016 IEEE 55th Conference on Decision and Control (CDC). Las Vegas, NV, USA, 2016: 4239-4244.

邵垒, 毛虹霖, 邢胜, 等. 高空风力发电发展现状及关键技术研究综述[J]. 新能源进展, 2020, 8(6): 477-485.

ARCHER C L, JACOBSON M Z.Evaluation of global wind power[J]. Journal of geophysical research: atmospheres, 2005, 110(D12).

贺园园, 方艳莹, 程雪玲, 等. 中国典型地形风能资源的湍流特征分析[J]. 太阳能学报, 2024, 45(7): 724-734.

唐圣学, 宋晓, 李明, 等. 光伏组件寿命衰退建模与寿命预测高斯随机过程回归方法研究[J]. 太阳能学报, 2022, 43(12): 41-49.

柳子然, 戴梓健, 岳程斐, 等. 基于高斯混合过程的空间机器人任务空间预测控制方法[J]. 系统工程与电子技术, 2023, 45(11): 3597-3605.

陈婕, 张怡, 房方, 等. 基于仿射扰动反馈的风力发电机组多目标随机模型预测控制[J]. 中国电机工程学报, 2023, 43(2): 496-507.

胡伟成, 杨庆山, 聂彪, 等. 复杂地形风电场微观选址的GA-PSO混合算法研究[J]. 太阳能学报, 2024, 45(5): 118-125.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于CEEMDAN和GA-BILSTM的锂离子电池剩余使用寿命预测

基于模糊电流观测的DAB变换器无模型预测控制

基于滑模扰动观测器的三相并网逆变器鲁棒模型预测控制

飞轮储能系统含参数辨识的双矢量交替顺序模型预测控制