针对漂浮式风电机组特点,提出一种适用于尾流优化控制的漂浮式风电场卷曲尾流模型。该模型针对漂浮式风电场内机组倾斜的尾流动态特征,基于准稳态卷曲尾流模型考虑倾斜与偏航偏差计算。在此基础上,提出一种模型预测尾流优化控制方法,以漂浮式风电场输出功率最大为目标协调各机组出力。在FAST.Farm中,将所建准稳态卷曲尾流模型（curl-N）与初始卷曲尾流模型（curl-O）、极性尾流模型（Polar）、大涡模拟（LES）计算的尾迹亏损速度对比,验证所建模型的准确性。为证明优化控制方法有效性,与无尾流优化、传统尾流优化进行出力特性对比,结果表明,所提准稳态卷曲尾流模型可适用于尾流优化控制,模型预测尾流优化控制可有效提高漂浮式风电场整场出力。
Abstract
A curled wake model for floating wind farms suitable for wake optimization control is proposed based on the characteristics of floating wind turbines. This model focuses on the dynamic characteristics of the inclined wake of units in floating wind farms, and considers the calculation of inclination and yaw deviation based on the quasi steady state curled wake model. On this basis, a model predictive wake optimization control method is proposed to coordinate the output of each unit with the goal of maximizing the output power of a floating wind farm. In FAST.Farm, the accuracy of the established models is verified by comparing the wake loss velocities calculated by the quasi steady state curled wake model （curl-N）, initial curled wake model （curl-O）, polar wake model （Polar）, and large eddy simulation （LES）. To demonstrate the effectiveness of the optimization control method, the output characteristics are compared with those of wake free optimization and traditional wake optimization. The results show that the proposed quasi steady state curled wake model can be applied to wake optimization control, and the model predicts that wake optimization control can effectively improve the overall output of floating wind farms.
关键词
海上风电场 /
尾流 /
模型预测控制 /
优化控制 /
发电量提升
Key words
offshore wind farms /
wakes /
model predictive control /
optimize control /
power generation increase
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基金
辽宁省揭榜挂帅科技攻关专项（2021JH1/10400009）
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