Effectiveness of data-driven wind turbine wake models developed by machine/deep learning with spatial-segmentation technique. (October 2022)

• Record Type: Journal Article
• Authors: Wang, Longyan; Xie, Junhang; Luo, Wei; Wang, Zilu; Zhang, Bowen; Chen, Meng; Tan, Andy C.C.
• Is Part Of: Sustainable energy technologies and assessments. Volume 53:Part A(2022)
• Publication Date: 2022-10
• Subjects: Wake modeling -- Artificial neural network -- Convolutional neural network -- Unet -- Spatial segmentation; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2022.102499
• Languages: English
• ISSNs: 2213-1388

Abstract

Highlights: Comparison of three neural network structures for constructing wind turbine wake model is made. Flow field spatial segmentation for reducing computational cost of network training is applied. Spatial-segmentation is effective in improving velocity prediction but not for turbulence intensity prediction. ANN structure is the best among all tested neural network structures. ANN- SS can yield the best prediction results for both velocity and turbulence intensity fields. Abstract: In this paper, the effectiveness of three machine/deep learning algorithms, namely, the artificial neural network (ANN), convolutional neural network (CNN) and U-shape neural network (Unet), in constructing wind turbine wake modeling is investigated. In order to enhance the performance of different neural networks, the spatial-segmentation technique for wake flow field is adopted which aims to divide the original wake field configuration (4D × 50D, D is the rotor diameter) into several small pieces (each with 4D × 6.25D). This is followed by separately training the subdivided small piece of wake flow fields and the resultant sub-models are consolidated to predict the whole wake flow field. Both wake velocity field and turbulence intensity field are predicted by the wake model to facilitate its applications to alleviate both wind turbine power losses and fatigue loads caused by wake interactions. Through comparative study, it is found that by using the spatial-segmentation technique it can significantly reduce the prediction error of the wake velocity but not for the prediction of turbulence intensity. Among the three selected network structures, ANN has the best prediction performance yielding the wake model with the maximum error of 11.6 % near to the rotor place, while for other regions it is generally below 8 %. By dividing the wake flow field into pieces, the maximum error located right behind the rotor reduces to 7.2 % with others less than 6 %. Through further repetitive training analysis, it proves a better and robust wake model can be achieved by ANN with the spatial segmentation. In comparison, the prediction error of turbulence intensity field is higher, but still fairly accurate for the far wake prediction with the error less than 5 %.