Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model
Short-term wind power forecasting is crucial for updating the wind power trading strategy, equipment protection and control regulation. To solve the difficulty surrounding the instability of the statistical model and the time-consuming nature of the physical model in short-term wind power forecastin...
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MDPI AG
2023-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/10/2223 |
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| author | Guangchao Zhang Shi Liu |
| author_facet | Guangchao Zhang Shi Liu |
| author_sort | Guangchao Zhang |
| collection | DOAJ |
| description | Short-term wind power forecasting is crucial for updating the wind power trading strategy, equipment protection and control regulation. To solve the difficulty surrounding the instability of the statistical model and the time-consuming nature of the physical model in short-term wind power forecasting, two innovative wind field reconstruction methods combining CFD and a reduced-order model were developed. In this study, POD and Tucker decomposition were employed to obtain the spatial–temporal information correlation of 2D and 3D wind fields, and their inverse processes were combined with sparse sensing to reconstruct multi-dimensional unsteady wind fields. Simulation and detailed discussion were performed to verify the practicability of the proposed algorithms. The simulation results indicate that the wind speed distributions could be reconstructed with reasonably high accuracy (where the absolute velocity relative error was less than 0.8%) using 20 sensors (which only accounted for 0.04% of the total data in the 3D wind field) based on the proposed algorithms. The factors influencing the results of reconstruction were systematically analyzed, including all-time steps, the number of basis vectors and 4-mode dimensions, the diversity of CFD databases, and the reconstruction time. The results indicated that the reconstruction time could be shortened to the time interval of data acquisition to synchronize data acquisition with wind field reconstruction, which is of great significance in the reconstruction of unsteady wind fields. Although there are still many studies to be carried out to achieve short-term predictions, both unsteady reconstruction methods proposed in this paper enable a new direction for short-term wind field prediction. |
| first_indexed | 2024-03-11T03:32:52Z |
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| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T03:32:52Z |
| publishDate | 2023-05-01 |
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| spelling | doaj.art-e91c4764d3ea416c968fd591a9502a0c2023-11-18T02:17:50ZengMDPI AGMathematics2227-73902023-05-011110222310.3390/math11102223Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order ModelGuangchao Zhang0Shi Liu1School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, ChinaSchool of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, ChinaShort-term wind power forecasting is crucial for updating the wind power trading strategy, equipment protection and control regulation. To solve the difficulty surrounding the instability of the statistical model and the time-consuming nature of the physical model in short-term wind power forecasting, two innovative wind field reconstruction methods combining CFD and a reduced-order model were developed. In this study, POD and Tucker decomposition were employed to obtain the spatial–temporal information correlation of 2D and 3D wind fields, and their inverse processes were combined with sparse sensing to reconstruct multi-dimensional unsteady wind fields. Simulation and detailed discussion were performed to verify the practicability of the proposed algorithms. The simulation results indicate that the wind speed distributions could be reconstructed with reasonably high accuracy (where the absolute velocity relative error was less than 0.8%) using 20 sensors (which only accounted for 0.04% of the total data in the 3D wind field) based on the proposed algorithms. The factors influencing the results of reconstruction were systematically analyzed, including all-time steps, the number of basis vectors and 4-mode dimensions, the diversity of CFD databases, and the reconstruction time. The results indicated that the reconstruction time could be shortened to the time interval of data acquisition to synchronize data acquisition with wind field reconstruction, which is of great significance in the reconstruction of unsteady wind fields. Although there are still many studies to be carried out to achieve short-term predictions, both unsteady reconstruction methods proposed in this paper enable a new direction for short-term wind field prediction.https://www.mdpi.com/2227-7390/11/10/2223proper orthogonal decompositiontucker decompositionwind field reconstructionspatial-temporal information correlationcomputational fluid dynamicsreduced-order model |
| spellingShingle | Guangchao Zhang Shi Liu Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model Mathematics proper orthogonal decomposition tucker decomposition wind field reconstruction spatial-temporal information correlation computational fluid dynamics reduced-order model |
| title | Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model |
| title_full | Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model |
| title_fullStr | Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model |
| title_full_unstemmed | Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model |
| title_short | Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model |
| title_sort | reconstruction of unsteady wind field based on cfd and reduced order model |
| topic | proper orthogonal decomposition tucker decomposition wind field reconstruction spatial-temporal information correlation computational fluid dynamics reduced-order model |
| url | https://www.mdpi.com/2227-7390/11/10/2223 |
| work_keys_str_mv | AT guangchaozhang reconstructionofunsteadywindfieldbasedoncfdandreducedordermodel AT shiliu reconstructionofunsteadywindfieldbasedoncfdandreducedordermodel |