ART-LSTANet: An adaptive intelligent method for wind turbine wake analysis

The analysis of wake effects within wind farms is paramount to elevating power generation efficiency, especially when considering the losses incurred by wake effects. In the present investigation, we introduce an innovative neural network-based model – adaptive reduction three-way long short term at...

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Veröffentlicht in:Engineering applications of artificial intelligence Jg. 126; S. 106809
Hauptverfasser: Xu, Li, Zhou, Guanhao, Guo, Zhaoliang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.11.2023
Schlagworte:
Attention mechanism
Deep neural network
Reduced-order model
Series forecasting algorithm
Wind turbine wake
Attention mechanism
Deep neural network
Series forecasting algorithm
Reduced-order model
Wind turbine wake
ISSN:0952-1976, 1873-6769
Online-Zugang:Volltext
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Zusammenfassung:The analysis of wake effects within wind farms is paramount to elevating power generation efficiency, especially when considering the losses incurred by wake effects. In the present investigation, we introduce an innovative neural network-based model – adaptive reduction three-way long short term attention network (ART-LSTANet) – designed to augment the precision of wind turbine wake flow field predictions. Unlike conventional methodologies that often segregate the reduced-order model from the prediction procedure, our proposed model exploits adaptive order reduction to swiftly procure the necessary input for the predictive model, thus curtailing the time expenditure throughout the entire process. The predictive model subsequently incorporates carefully designed feature extraction components tailored to multiple temporal scales, with parameters being updated via a data-driven approach. A comparative analysis with six established intelligent algorithms underscores the superiority of the ART-LSTANet. Comprehensive results indicate that ART-LSTANet delivers superior performance in the reconstruction of the wake flow field, demonstrating a reduction in the mean squared error by up to 9.0% and in the root mean squared error by up to 3.3% compared to traditional methodologies. Numerical errors calculated under a spectrum of additional evaluation metrics consistently yield the lowest values. [Display omitted] •We propose an intelligent algorithm for adaptively analyzing wind turbine wake.•A standalone Reduction Block module replaces pre-order reduced-order model.•We design a three-way neural network to capture data correlation across time scales.•The proposed model is compared to six models for turbine wake prediction.•The proposed model is tested in two different inflow velocities for prediction.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2023.106809