The importance of examining the wake effect of wind farms for optimizing their layout and augmenting their power generation efficiency is immense. Considering that the establishment of extensive wind farms often leads...
详细信息
The importance of examining the wake effect of wind farms for optimizing their layout and augmenting their power generation efficiency is immense. Considering that the establishment of extensive wind farms often leads to a significant number of turbines being positioned downstream of preceding ones, it significantly diminishes their power generation efficiency. In our study, we propose a graph representation learning model with improved Transformer (GRL-ITransformer) to better integrate feature information, so that the model can capture the dynamic time relationship of different variables and establish its spatial relationship, striving to enhance the precision in predicting wind turbine wake field. Different from the previous way involving handling reduced-order and separating prediction process, we combine the reduced-order technique with the proposed model to make the model more efficiently and intelligently determine the number of modes required for model prediction. After that, the data driven method is employed to update the parameters, and the superiority of GRL-ITransformer is highlighted by analyzing and comparing with the existing five classical intelligent algorithms (belongs to four categories). The comprehensive results show that GRL-ITransformer has excellent performance in wind turbine wake field prediction and reconstruction, and always possesses the lowest error for a series of error evaluation indexes among all models.
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 inno...
详细信息
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.
暂无评论