Power Curve Modelling for Wind Turbines

A. Teyabeen, F. Akkari, A. Jwaid
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引用次数: 28

Abstract

The wind turbine power curve WTPC describes the relationship between wind speed and turbine power output. Power curve, provided by the manufacturer is one of the most important tools used to estimate turbine power output and capacity factor. Hence, an accurate WTPC model is essential for predicting wind energy potential. This paper presents a comparative study of various models for mathematical modelling of WTPC based on manufacturer power curve data gathered from 32 wind turbines ranging from 330 to 7580 kW. The selected models are validated by comparing the capacity factor obtained using the models based on Gamma probability density function with the capacity factor estimated using manufacturer power curves based on measured wind speed data. The selected models are also validated by comparing the instantaneous power obtained using the models with manufacturer power curve data. The accuracy of the models is evaluated using statistical criteria such as Normalized Root Mean Square Error (NRMSE), relative error (RE), and correlation coefficient ( ). The adopted model allows predicting the behavior of wind turbine generated under different wind speeds. Results of the analysis presented in this paper show that the power-coefficient based model presents favorable efficiency followed by general model, since they have lower values of RE in estimation of capacity factor, whereas the polynomial model showed the least accurate model.
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风力发电机功率曲线建模
风力机功率曲线WTPC描述了风速与风力机输出功率之间的关系。由制造商提供的功率曲线是估算汽轮机功率输出和容量系数的重要工具之一。因此,准确的WTPC模型对于预测风能潜力至关重要。本文以330 ~ 7580 kW的32台风力发电机组的生产厂家功率曲线数据为基础,对WTPC数学建模的各种模型进行了比较研究。通过比较基于伽马概率密度函数的模型得到的容量因子与基于实测风速数据的制造商功率曲线估计的容量因子,对所选模型进行了验证。通过与厂家功率曲线数据的比较,对所选模型进行了验证。使用标准化均方根误差(NRMSE)、相对误差(RE)和相关系数()等统计标准来评估模型的准确性。所采用的模型可以预测风力机在不同风速下的性能。本文的分析结果表明,基于功率系数的模型效率较好,其次是一般模型,因为它们在估计容量因子时的RE值较低,而多项式模型的模型精度最低。
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