GRL-ITransformer: An Intelligent Method for Multi-Wind-Turbine Wake Analysis Based on Graph Representation Learning With Improved Transformer
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,...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10916666/ |
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| author | Kang Han Li Xu |
| author_facet | Kang Han Li Xu |
| author_sort | Kang Han |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-89ca64fc0f7f4279a3959eb1808f3fb1 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-89ca64fc0f7f4279a3959eb1808f3fb12025-08-20T02:55:49ZengIEEEIEEE Access2169-35362025-01-0113435724359210.1109/ACCESS.2025.354903510916666GRL-ITransformer: An Intelligent Method for Multi-Wind-Turbine Wake Analysis Based on Graph Representation Learning With Improved TransformerKang Han0https://orcid.org/0009-0004-8344-7007Li Xu1https://orcid.org/0000-0002-9468-7629College of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, ChinaCollege of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, ChinaThe 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.https://ieeexplore.ieee.org/document/10916666/Wind turbine wakesreduced-order modelimproved transformerattention mechanismgraph representation learningseries forecasting algorithm |
| spellingShingle | Kang Han Li Xu GRL-ITransformer: An Intelligent Method for Multi-Wind-Turbine Wake Analysis Based on Graph Representation Learning With Improved Transformer IEEE Access Wind turbine wakes reduced-order model improved transformer attention mechanism graph representation learning series forecasting algorithm |
| title | GRL-ITransformer: An Intelligent Method for Multi-Wind-Turbine Wake Analysis Based on Graph Representation Learning With Improved Transformer |
| title_full | GRL-ITransformer: An Intelligent Method for Multi-Wind-Turbine Wake Analysis Based on Graph Representation Learning With Improved Transformer |
| title_fullStr | GRL-ITransformer: An Intelligent Method for Multi-Wind-Turbine Wake Analysis Based on Graph Representation Learning With Improved Transformer |
| title_full_unstemmed | GRL-ITransformer: An Intelligent Method for Multi-Wind-Turbine Wake Analysis Based on Graph Representation Learning With Improved Transformer |
| title_short | GRL-ITransformer: An Intelligent Method for Multi-Wind-Turbine Wake Analysis Based on Graph Representation Learning With Improved Transformer |
| title_sort | grl itransformer an intelligent method for multi wind turbine wake analysis based on graph representation learning with improved transformer |
| topic | Wind turbine wakes reduced-order model improved transformer attention mechanism graph representation learning series forecasting algorithm |
| url | https://ieeexplore.ieee.org/document/10916666/ |
| work_keys_str_mv | AT kanghan grlitransformeranintelligentmethodformultiwindturbinewakeanalysisbasedongraphrepresentationlearningwithimprovedtransformer AT lixu grlitransformeranintelligentmethodformultiwindturbinewakeanalysisbasedongraphrepresentationlearningwithimprovedtransformer |