A novel dynamic wind farm wake model based on deep learning. (1st November 2020)

• Record Type: Journal Article
• Authors: Zhang, Jincheng; Zhao, Xiaowei
• Is Part Of: Applied energy. Volume 277(2020)
• Publication Date: 2020-11-01
• Subjects: CFD simulation -- Deep learning -- Dynamic wake model -- Reduced order modelling -- Wind farm control; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.115552
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A novel dynamic wind farm wake model is developed based on deep learning. This model can predict unsteady flow features while running as fast as static model. A ROM method for unsteady distributed fluid systems is proposed to build the model. A high-fidelity CFD database for wind farm is generated for model training. Abstract: A deep learning based reduced order modelling method for general unsteady fluid systems is proposed, which is then applied to develop a novel dynamic wind farm wake model. The proposed method employs the proper orthogonal decomposition technique for reducing the flow field dimension and the long short-term memory network for predicting the reduced representation of the flow field at a future time step. The method is specifically designed to tackle distributed fluid systems (such as wind farm wakes) and to be control-oriented. For wind farm wake modelling, a set of large eddy simulations are first carried out to generate a series of flow field data for wind turbines operating in different conditions. Then the proposed method is employed to develop the data-based wake model. The results show that this novel dynamic wind farm wake model can predict the main features of unsteady wind turbine wakes similarly as high-fidelity wake models while running as fast as the low-fidelity static wake models and that the model's overall prediction error is just 4.8 % with respect to the freestream wind speed. As an illustrative example, the developed model can predict the unsteady turbine wakes of a 9-turbine test wind farm within several seconds based on a standard desktop while it requires tens of thousands of CPU hours on a high-performance computing cluster if a high-fidelity model is used. Thus the developed model can be used for fast yet accurate simulation of wind farms as well as for their predictions and control designs.