Detailed Analysis Method of the On-site Wind Resource Data for Large-scale Wind Farm
The development of the large-scale wind farm or wind power base has become the main stream and innovative features under the"dual carbon goals"strategy in China.It is of great significance to investigate the regional wind variation and thermal status of the atmospheric boundary layer flow over the long-span wind site,in order to perform the wind resource assessment accurately and conduct the wind turbine micro-sitting optimally.Aiming at the special circumstance of the wind resource development,a detailed analysis method of the on-site wind resource data from multiple measure locations for the large-scale wind farm was proposed.The conditional wind statistics was filtered based on the reference met-tower and the data with local atmospheric stability were sorted out.The wind data sets under neutral,stable and unstable stabilities corresponded to the different met-towers were established accordingly.The threshold of turbulence intensity for the neutral stability driven by the conditional wind was explored.The special variations of wind flow within the wind farm site under different atmospheric stabilities were investigated.The results show that the wind shear and turbulence intensity level at the reference met-tower location show increasing and decreasing trends,respectively,along with the atmosphere changes from unstable to stable states driven by the conditional wind.The mean wind speed and turbulence intensity profiles are quite different from those obtained by the ensemble average of the datasets with all stability states.The wind statistic differences at each met-tower between the unstable and neutral atmospheres,show more obvious than those between stable and neutral atmosphere.It is concluded that the wind datasets of multiple met-tower locations obtained by the proposed method can provide quantitative data verification for the adaptive development and improvement of the turbulence model in numerical method under specific wind driving conditions.
万方数据
维普
