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Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine

机译:模糊建模技术和人工神经网络来估算风力发电机的年发电量

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The purpose of this article is to develop a new method to estimate annual energy output for a given wind turbine in any region which should be easy to use and has satisfactory accuracy. To do this, hourly wind speeds of 25 different stations in Netherlands, output power curve of S47 wind turbine and fuzzy modeling techniques and artificial neural networks were used and a model is developed to estimate annual energy output for S47 wind turbine in different regions. Since this model has three inputs (average wind speed, standard deviation of wind speed, and air density of that region), this model is easy to use. The accuracy of this method is compared with the accuracy of conventional methods and it is shown that this new method performs better. Thereafter, we have shown that by making some small changes to this proposed model, other pitch control wind turbines could be modeled too. As an example, we have modeled E82 wind turbine based on the model developed for S47 and it is shown that this model has still satisfactory accuracy.
机译:本文的目的是开发一种新方法,用于估算给定风力涡轮机在任何区域内的年度能量输出,该方法应易于使用且具有令人满意的精度。为此,使用了荷兰25个不同站点的每小时风速,S47风力发电机的输出功率曲线,模糊建模技术和人工神经网络,并开发了一个模型来估算不同地区S47风力发电机的年发电量。由于该模型具有三个输入(平均风速,风速标准偏差和该区域的空气密度),因此该模型易于使用。将该方法的准确性与常规方法的准确性进行了比较,结果表明该新方法性能更好。此后,我们已经表明,通过对该提议的模型进行一些小的更改,也可以对其他变桨控制风力涡轮机进行建模。例如,我们基于为S47开发的模型对E82风力发电机进行建模,结果表明该模型仍具有令人满意的精度。

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