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
风能捕获 /
模型预测控制 /
遗传算法 /
高空风能 /
混合高斯过程
Key words
energy harvesting /
model predictive control /
genetic algorithms /
airborne wind energy /
mixed Gaussian processes
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参考文献
[1] 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.
LIU Y S, WANG L M, MENG E L, et al.Dynamic adjustment performance of wind turbines based on measured data[J]. Power system technology, 2020, 44(11): 4328-4341.
[2] 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.
[3] BECHTLE P, SCHELBERGEN M, SCHMEHL R, et al.Airborne wind energy resource analysis[J]. Renewable energy, 2019, 141: 1103-1116.
[4] 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.
HE Y L.Application research on directly driven permanent magnet generator in high-altitude wind power[D]. Harbin: Harbin Institute of Technology, 2011.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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
[12] VERMILLION C, FAGIANO L.Electricity in the air: tethered wind energy systems[J]. Mechanical engineering, 2013, 135(9): 13-21.
[13] 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.
[14] 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.
[15] 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.
SHAO L, MAO H L, XING S, et al.Review on development status and key technology of airborne wind energy system[J]. Advances in new and renewable energy, 2020, 8(6): 477-485.
[16] ARCHER C L, JACOBSON M Z.Evaluation of global wind power[J]. Journal of geophysical research: atmospheres, 2005, 110(D12).
[17] 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.
HE Y Y, FANG Y Y, CHENG X L, et al.Study on turbulence characteristics of wind energy resources in China’s typical terrain[J]. Acta energiae solaris sinica, 2024, 45(7): 724-734.
[18] 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.
TANG S X, SONG X, LI M, et al.Research on lifetime degradation modeling and Gaussian stochastic process regression method for life prediction of photovoltaic modules[J]. Acta energiae solaris sinica, 2022, 43(12): 41-49.
[19] 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.
LIU Z R, DAI Z J, YUE C F, et al.Gaussian-mixture-process-based task-space predictive control method for space robot[J]. Systems engineering and electronics, 2023, 45(11): 3597-3605.
[20] 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.
CHEN J, ZHANG Y, FANG F, et al.Affine-disturbance-feedback-based multi-objective stochastic model predictive control of wind turbine[J]. Proceedings of the CSEE, 2023, 43(2): 496-507.
[21] 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.
HU W C, YANG Q S, NIE B, et al.Hybrid GA-PSO algorithm for wind farm micro-siting in complex terrain[J]. Acta energiae solaris sinica, 2024, 45(5): 118-125.
基金
国家重点研发计划（2023YFB4203404）
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