基于卷积神经网络和简单循环单元集成模型的风电场内多风机风速预测

doi:10.19595/j.cnki.1000-6753.tces.191715

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Abstract Conventional wind speed forecasts focused on the overall wind speed of a wind farm. However, in an actual wind farm, multiple wind turbines are widely distributed in different geographical locations, and their local wind speeds are significantly different. To address this, this paper proposes a method for the turbine-specific wind speed forecasts in a wind farm. This method learns the historical spatial and temporal correlation between wind speed and wind direction. Firstly, the convolutional neural network (CNN) is used to extract the spatial correlation information among multiple wind turbines. Subsequently, the extracted spatial information is processed by the simple recurrent unit (SRU), which learns the temporal correlation information. During the data preprocess procedure, wind speed and trigonometric wind direction form a three-dimensional matrix, which is similar to the RGB image series. CNN is very suitable for processing data of RGB image type, so the spatial information of wind speed and wind direction of multiple wind turbines at the same moment is extracted by CNN. Compared with other recurrent neural network (RNN), SRU has much lower computational cost. So SRU is utilized to extract the dynamic information of wind speed and wind direction of multiple wind turbines over time. The simulation results on actual wind farm data validate the effectiveness of the proposed method.

Key words： Wind speed forecast of multiple wind turbines convolutional neural network simple recurrent unit improved recurrent neural network spatio-temporal correlation

Received: 08 December 2019

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	Wang Chen
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Cite this article:

Wang Chen,Kou Peng. Wind Speed Forecasts of Multiple Wind Turbines in a Wind Farm Based on Integration Model Built by Convolutional Neural Network and Simple Recurrent Unit[J]. Transactions of China Electrotechnical Society, 2020, 35(13): 2723-2735.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.191715 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I13/2723

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