Improving Site-Dependent Wind Turbine Performance Prediction Accuracy Using Machine Learning

IF 1.8 Q2 ENGINEERING, MULTIDISCIPLINARY
S. Barber, F. Hammer, A. Tica
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引用次数: 7

Abstract

Data-driven wind turbine performance predictions, such as power and loads, are important for planning and operation. Current methods do not take site-specific conditions such as turbulence intensity and shear into account, which could result in errors of up to 10%. In this work, four different machine learning models (k-nearest neighbors regression, random forest regression, extreme gradient boosting regression and artificial neural networks (ANN) are trained and tested, firstly on a simulation dataset and then on a real dataset. It is found that machine learning methods that take site-specific conditions into account can improve prediction accuracy by a factor of two to three, depening on the error indicator chosen. Similar results are observed for multi-output ANNs for simulated in- and out-of-plane rotor blade tip deflection and root loads. Future work focuses on understanding transferability of results between different turbines within a wind farm and between different wind turbine types.
利用机器学习提高现场风力涡轮机性能预测的准确性
数据驱动的风力涡轮机性能预测,如功率和负荷,对规划和运行很重要。目前的方法没有考虑到现场特定的条件,如湍流强度和剪切,这可能导致高达10%的误差。在这项工作中,四种不同的机器学习模型(k-最近邻回归,随机森林回归,极端梯度增强回归和人工神经网络(ANN))进行了训练和测试,首先在模拟数据集上,然后在真实数据集上。研究发现,考虑到特定地点条件的机器学习方法可以将预测精度提高两到三倍,具体取决于所选择的误差指标。多输出人工神经网络在模拟面内和面外旋翼叶尖挠度和根部载荷时也观察到类似的结果。未来的工作重点是了解风力发电场内不同涡轮机之间以及不同风力涡轮机类型之间结果的可转移性。
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
CiteScore
5.20
自引率
13.60%
发文量
34
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