Three-dimensional wind velocity reconstruction based on tensor decomposition and CFD data with experimental verification

Wind energy, which has many advantages, is in a stage of rapid development. However, because wind is sporadic and random, it is difficult to ensure a stable and efficient power supply, which poses risks to the security and stability of the power system. Therefore, research on short-term wind prediction is of great importance. Previous forecasting methods based on vectors or matrices have only been applied to wind velocity distributions in twodimensional planes. If applied to multiple planes in three-dimensional (3D) space, these methods may not accurately reflect wind velocity distributions. To address this, we propose a novel method of wind forecasting: a tensor-based method that combines Tucker decomposition and computational fluid dynamics (CFD) to rapidly reconstruct 3D wind velocity distributions. A fourth-order wind velocity tensor database under three terrains is established by CFD simulation, then dimensionality reduction and feature extraction are carried out on the database by Tucker decomposition. The coefficient tensors obtained by decomposition are used to rapidly reconstruct 3D wind velocity distributions. Wind fields are successfully reconstructed with good accuracy for direction angles ranging from 0 to 180 and inlet speeds ranging from 0 to 33 m/s. The influences of core tensor dimension, the number and distribution of sensors, and noise on reconstruction error are discussed in the error analysis. Ultimately, the proposed method is verified by anemometer values from a wind tunnel experiment. The minimum relative reconstruction error is 1.79%. The experimental results show that the proposed method can accurately reconstruct 3D wind velocity distributions in wind fields and is an innovative method of short-term wind forecasting.