利用海上风电场 SCADA 数据校准尾流分析模型的超参数调整框架

Diederik van Binsbergen, P. Daems, T. Verstraeten, Amir R. Nejad, J. Helsen
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引用次数: 1

摘要

摘要本研究提出了一种用于校准分析型尾流模型的稳健方法,并利用一个海上风电场的 4 年时间序列监控和数据采集(SCADA)数据对高斯-卷尔混合模型的速度缺失参数进行了演示,同时采用了树状结构的 Parzen 估计器作为采样器。首先,对尾流参数及其线性相关关系进行了敏感性分析。采用湍流强度为 0.06 的唤醒模型,不考虑阻塞模型。结果表明,与不考虑湍流强度的调整参数相比,乘以特定湍流强度的调整参数具有更高的灵敏度。此外还发现,当流入风条件受到邻近风电场的影响时,优化收敛的残余误差会更大。在对邻近风电场的风机发电量进行比较时,这种影响的重要性就显而易见了。灵敏度高的参数收敛性强,而灵敏度低的参数在优化后会出现显著差异。此外,沿海影响也会影响校准结果,来自陆地的风比来自海洋的风导致更快的尾流恢复。鉴于本研究假设湍流强度恒定,因此在使用更具代表性的特定地点湍流强度测量结果作为模型输入时,需要重新校准。在未考虑基本模型假设和具体地点特征的情况下使用这些结果时应谨慎,因为如果不进一步重新校准,这些结果可能无法推广到其他地点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm
Abstract. This work presents a robust methodology for calibrating analytical wake models, as demonstrated on the velocity deficit parameters of the Gauss–curl hybrid model using 4 years of time series supervisory control and data acquisition (SCADA) data from an offshore wind farm, with a tree-structured Parzen estimator employed as a sampler. Initially, a sensitivity analysis of wake parameters and their linear correlation is conducted. The wake model is used with a turbulence intensity of 0.06, and no blockage model is considered. Results show that the tuning parameters that are multiplied by the turbine-specific turbulence intensity pose higher sensitivity than tuning parameters not giving weight to the turbulence intensity. It is also observed that the optimization converges with a higher residual error when inflow wind conditions are affected by neighbouring wind farms. The significance of this effect becomes apparent when the energy yield of turbines situated in close proximity to nearby wind farms is compared. Sensitive parameters show strong convergence, while parameters with low sensitivity show significant variance after optimization. Additionally, coastal influences are observed to affect the calibrated results, with wind from land leading to faster wake recovery than wind from the sea. Given the assumption of constant turbulence intensity in this work, recalibration is required when more representative site-specific turbulence intensity measurements are used as input to the model. Caution is advised when using these results without considering underlying model assumptions and site-specific characteristics, as these findings may not be generalizable to other locations without further recalibration.
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