风切变不确定性对海上单桩风力发电机长期极端响应的影响

D. Barreto, M. Karimirad, A. Ortega
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引用次数: 0

摘要

在海洋结构随机动力学领域,确定长期极端响应是保证结构可靠度的关键。这些响应的计算需要精确的环境条件知识和可靠的方法来预测与可靠性目标水平相关的值。虽然有一种非常精确的方法来确定这些极值的值,例如全长期分析(FLTA),但这种方法的计算成本很高。然后,需要近似的方法。一种确定与极值计算最相关的环境条件的实用方法是环境等高线法(ECM)。然而,当这种方法用于考虑生存策略的海上结构(如海上风力涡轮机)时,发现了一些局限性。最后,开发了一种改进的ECM程序(MECM),目的是绕过传统ECM的局限性。该方法基于短期模拟,通过在操作范围内测试许多环境轮廓的迭代过程,可以找到具有相应返回周期的重要风速,从而避免了传统ECM所存在的问题。由大量参数表示的环境条件也是极值计算的一个重要方面。其中一些被视为随机值,而另一些被认为是确定性的,因此它们的测量值/估计值中存在不确定性是不可避免的。通过采用标准和准则所建议的值来解决这些不确定性,在实践中,当缺乏关于所研究的具体地点的信息时,通常需要保持保守。因此,了解这些不确定性对控制海上结构(尤其是风力涡轮机)设计的载荷/响应的影响是非常重要的。本文研究了不确定性对风切变系数的影响。该参数与一个关键环境条件直接相关,即轮毂高度处的风速,其对发电和疲劳载荷的影响已被文献记载,但很少有案例涉及它们对底部固定OWT响应的影响。这项工作旨在强调准确选择剪切系数的相关性,以及考虑到可能出现与推荐值相当大的差异,剪切系数对底部固定OWT概率分析的影响。通过FAST中的耦合模拟,NREL 5MW风电机组将受到不同的风切变条件,并考虑MECM的影响,计算相应的50年长期响应,以考虑风电机组生存模式的影响。用全局极值法拟合极值。最后,试图将相关输入参数(即WSC)的不确定性与传播到输出参数(即外推的长期极端响应)的不确定性联系起来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Influence of Wind Shear Uncertainty in Long-Term Extreme Responses of an Offshore Monopile Wind Turbine
In the field of stochastic dynamics of marine structures, the determination of long-term extreme responses is a crucial aspect to ensure the desired level of structural reliability. The calculation of these responses requires precise knowledge of the environmental conditions and reliable methods to predict the values associated with a reliability target level. While there is a very precise method to determine the value of these extreme values, e. g. the full long-term analysis (FLTA), this approach is computationally expensive. Then, approximated methods are needed. One practical approach for the determination of the most relevant environmental conditions for extreme calculation is the environmental contour method (ECM). However, some limitations have been detected when this method is used for offshore structures that consider survival strategies e. g. offshore wind turbines (OWT). Lastly, a modified ECM procedure (MECM) has been developed with the purpose to bypass the limitations of the traditional ECM. This method is based on short-term simulations and through an iterative process by testing many environmental contours in the operational range allows finding an important wind speed with its corresponding return period and thus, the problem that traditional ECM has, is avoided. The environmental conditions, which are represented by a large number of parameters, are also an important aspect of extreme calculation. Whereas some of them are treated as stochastic values, some are considered deterministic and, therefore the existence of uncertainties in their measured/estimated values is inevitable. These uncertainties are addressed by adopting values recommended by standards and guidelines and, in practice, it is often necessary to be conservative when there is a lack of information about the specific site studied. Therefore, the understanding of the impact that these uncertainties can have on the loads/responses that govern the design of offshore structures, especially wind turbines, is of great relevance. In this work, the influence of uncertainty in the wind shear coefficient (WSC) is studied. This parameter is directly related to one critical environmental condition i. e. wind speed at hub height, and its influence in power production and fatigue loads has been documented in the literature, but, few cases have addressed their influence in bottom fixed OWT responses. This work seeks to highlight the relevance of an accurate selection of shear coefficient and, its influence on the probabilistic analysis of a bottom fixed OWT taking into account that considerable variations from recommended values may occur. Through the use of coupled simulations in FAST, the NREL 5MW wind turbine will be subjected to varying wind shear conditions, and the corresponding 50-yr long-term responses will be calculated considering the MECM to take into account the influence of the wind turbine survival mode. The extreme values are fitted from a Global Maxima Method (GMM). Finally, it is sought to relate the uncertainty in a relevant input parameter (i. e. WSC) with the uncertainties propagated to the output parameters (i. e. extrapolated long-term extreme responses).
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