Three-dimensional wind field reconstruction using tucker decomposition with optimal sensor placement

Short-term wind prediction is of great significance for wind power trading strategy, equipment protection, and effective control. Physical models can provide accurate and detailed wind speed data but require a large amount of time for calculation. To solve this problem, based on Tucker decomposition, a sensor and CFD (Computational Fluid Dynamics) data fusion technique is proposed, which allows us to readily obtain an estimation of the ‘actual’ three-dimensional wind field from sensor observations. Moreover, a new greedy algorithm based on minimum condition is developed to stabilize the inverse process and derive optimal sensor placement. In the simulation, the ‘actual’ wind field can be reconstructed with reasonable accuracy for different wind shear characteristics, and the relative errors of absolute velocity is less than 0.2%. Additionally, the reconstruction time is much less than ultra-short-term forecasting and accounting for only 0.2%–3.2% of the CFD calculation time. The experimental results show that the optimal placement is much more effective than random placement in terms of minimizing the relative error (the relative error can be controlled within 6%). In general, the innovative approach combines the advantages of both statistical and physical approaches while compensating for their shortcomings, making it potentially valuable in short-term wind forecasting.