期刊
ENERGY CONVERSION AND MANAGEMENT
卷 256, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.enconman.2022.115322
关键词
Wind field reconstruction; Tucker decomposition; Computational fluid dynamics; Fourth-order tensor database; Three-dimensional velocity distributions
资金
- National Natural Science Foundation of China [61871181]
Wind energy is rapidly developing, but the sporadic and random nature of wind poses challenges to stable and efficient power supply. This paper proposes a tensor-based method that combines Tucker decomposition and computational fluid dynamics to reconstruct 3D wind velocity distributions. The proposed method accurately reconstructs wind fields and offers an innovative approach to short-term wind forecasting.
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.
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