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Sensitivity analysis of the effect of wind characteristics and turbine properties on wind turbine loads

机译:风力特性和涡轮机性能对风力涡轮机负荷影响的敏感性分析

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Proper wind turbine design relies on the ability to accurately predict ultimate and fatigue loads of turbines. The load analysis process requires precise knowledge of the expected wind-inflow conditions as well as turbine structural and aerodynamic properties. However, uncertainty in most parameters is inevitable. It is therefore important to understand the impact such uncertainties have on the resulting loads. The goal of this work is to assess which input parameters have the greatest influence on turbine power, fatigue loads, and ultimate loads during normal turbine operation. An elementary effects sensitivity analysis is performed to identify the most sensitive parameters. Separate case studies are performed on (1)?wind-inflow conditions and (2)?turbine structural and aerodynamic properties, both cases using the National Renewable Energy Laboratory 5 MW baseline wind turbine. The Veers model was used to generate synthetic International Electrotechnical Commission (IEC) Kaimal turbulence spectrum inflow. The focus is on individual parameter sensitivity, though interactions between parameters are considered. The results of this work show that for wind-inflow conditions, turbulence in the primary wind direction and shear are the most sensitive parameters for turbine loads, which is expected. Secondary parameters of importance are identified as veer, u-direction integral length, and u components of the IEC coherence model, as well as the exponent. For the turbine properties, the most sensitive parameters are yaw misalignment and outboard lift coefficient distribution; secondary parameters of importance are inboard lift distribution, blade-twist distribution, and blade mass imbalance. This information can be used to help establish uncertainty bars around the predictions of engineering models during validation efforts, and provide insight to probabilistic design methods and site-suitability analyses.
机译:适当的风力涡轮机设计依赖于准确预测涡轮机的最终和疲劳负载的能力。负载分析过程需要精确地了解预期的风力流入条件以及涡轮机结构和空气动力学性质。但是,大多数参数的不确定性是不可避免的。因此,了解这种不确定性对所得负荷的影响是重要的。这项工作的目标是评估涡轮机动力,疲劳负荷和常规涡轮机操作期间对涡轮功率,疲劳负载和最终负载有最大的影响。进行基本效应敏感性分析以识别最敏感的参数。单独的案例研究是在(1)?风流入条件和(2)?涡轮机结构和空气动力学性质,两种情况下使用全国可再生能源实验室5 MW基线风力涡轮机。 VEERS模型用于生成合成国际电工委员会(IEC)KAIMAL湍流谱流入。焦点是各个参数灵敏度,尽管考虑参数之间的交互。这项工作的结果表明,对于风力流入条件,初级风向和剪切中的湍流是涡轮机负载最敏感的参数,这是预期的。重要的重要参数被识别为VEER,U方向整数长度和IEC相干模型的U分量,以及指数。对于涡轮机性质,最敏感的参数是横出偏移和外侧提升系数分布;重要的重要性参数是舷内提升分布,刀片捻度和叶片质量不平衡。该信息可用于帮助在验证工作中的工程模型预测周围建立不确定性条,并提供概率的概率设计方法和站点适用性分析。

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