Sensitivity analysis of wake steering optimisation for wind farm power maximisation

IF 3.6 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
Filippo Gori, Sylvain Laizet, Andrew Wynn
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Abstract

Abstract. Modern large-scale wind farms consist of multiple turbines clustered together, usually in well-structured formations. Clustering has a number of drawbacks during a wind farm's operation, as some of the downstream turbines will inevitably operate in the wake of those upstream, with a significant reduction in power output and an increase in fatigue loads. Wake steering, a control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, is a promising strategy to mitigate power losses. The purpose of this work is to investigate the sensitivity of open-loop wake steering optimisation in which an internal predictive wake model is used to determine the farm power output as a function of the turbine yaw angles. Three different layouts are investigated with increasing levels of complexity. A simple 2×1 farm layout under aligned conditions is first considered, allowing for a careful investigation of the sensitivity to wake models and operating conditions. A medium-complexity case of a generic 5×5 farm layout under aligned conditions is examined to enable the study of a more complex design space. The final layout investigated is the Horns Rev wind farm (80 turbines), for which there have been very few studies of the performance or sensitivity of wake steering optimisation. Overall, the results indicate a strong sensitivity of wake steering strategies to both the analytical wake model choice and the particular implementation of algorithms used for optimisation. Significant variability can be observed in both farm power improvement and optimal yaw settings, depending on the optimisation setup. Through a statistical analysis of the impact of optimiser initialisation and a study of the multi-modal and discontinuous nature of the underlying farm power objective functions, this study shows that the uncovered sensitivities represent a fundamental challenge to robustly identifying globally optimal solutions for the high-dimensional optimisation problems arising from realistic wind farm layouts. This paper proposes a simple strategy for sensitivity mitigation by introducing additional optimisation constraints, leading to higher farm power improvements and more consistent, coherent, and practicable optimal yaw angle settings.
风电场功率最大化尾流转向优化的灵敏度分析
摘要现代大型风力发电场由多个涡轮机聚集在一起组成,通常结构良好。在风力发电场的运行过程中,集群有很多缺点,因为一些下游的涡轮机将不可避免地跟随上游的涡轮机运行,这将导致功率输出的显著减少和疲劳负荷的增加。尾流转向是一种控制策略,它使上游风力涡轮机与风错位,使其尾流远离下游涡轮机,这是一种很有前途的减少功率损失的策略。这项工作的目的是研究开环尾流转向优化的敏感性,其中使用内部预测尾流模型来确定作为涡轮偏航角的函数的农场功率输出。三种不同的布局随着复杂度的增加而被研究。首先考虑在对齐条件下的简单2×1农场布局,允许仔细调查对尾流模型和操作条件的敏感性。中等复杂的情况下,一个通用的5×5农场布局在对齐条件下进行检查,使研究更复杂的设计空间。最终研究的布局是Horns Rev风电场(80台涡轮机),很少有关于尾流转向优化性能或灵敏度的研究。总体而言,结果表明尾流转向策略对分析尾流模型选择和用于优化的算法的特定实现都具有很强的敏感性。根据优化设置,在机场功率改进和最佳偏航设置中都可以观察到显著的可变性。通过对优化器初始化影响的统计分析,以及对潜在风电场目标函数的多模态和不连续性质的研究,本研究表明,未发现的敏感性代表了对现实风电场布局引起的高维优化问题的鲁棒性识别全局最优解的基本挑战。本文提出了一种简单的策略,通过引入额外的优化约束来降低灵敏度,从而提高机场功率和更一致、连贯和可行的最佳偏航角设置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Wind Energy Science
Wind Energy Science GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY-
CiteScore
6.90
自引率
27.50%
发文量
115
审稿时长
28 weeks
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