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A novel dynamic wind farm wake model based on deep learning

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  • Zhang, Jincheng
  • Zhao, Xiaowei

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.

Suggested Citation

  • Zhang, Jincheng & Zhao, Xiaowei, 2020. "A novel dynamic wind farm wake model based on deep learning," Applied Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:appene:v:277:y:2020:i:c:s0306261920310643
    DOI: 10.1016/j.apenergy.2020.115552
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    4. Gao, Xiaoxia & Zhang, Shaohai & Li, Luqing & Xu, Shinai & Chen, Yao & Zhu, Xiaoxun & Sun, Haiying & Wang, Yu & Lu, Hao, 2022. "Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test," Energy, Elsevier, vol. 254(PA).
    5. Zhang, Jincheng & Zhao, Xiaowei, 2022. "Wind farm wake modeling based on deep convolutional conditional generative adversarial network," Energy, Elsevier, vol. 238(PB).
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    7. Zhou, Lei & Wen, Jiahao & Wang, Zhaokun & Deng, Pengru & Zhang, Hongfu, 2023. "High-fidelity wind turbine wake velocity prediction by surrogate model based on d-POD and LSTM," Energy, Elsevier, vol. 275(C).
    8. Luo, Zhaohui & Wang, Longyan & Xu, Jian & Wang, Zilu & Yuan, Jianping & Tan, Andy C.C., 2024. "A reduced order modeling-based machine learning approach for wind turbine wake flow estimation from sparse sensor measurements," Energy, Elsevier, vol. 294(C).
    9. James Roetzer & Xingjie Li & John Hall, 2024. "Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads," Energies, MDPI, vol. 17(16), pages 1-20, August.
    10. Zhiwen Deng & Chang Xu & Zhihong Huo & Xingxing Han & Feifei Xue, 2023. "Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model," Energies, MDPI, vol. 16(9), pages 1-20, May.
    11. Kuichao Ma & Mohsen Soltani & Amin Hajizadeh & Jiangsheng Zhu & Zhe Chen, 2021. "Wind Farm Power Optimization and Fault Ride-Through under Inter-Turn Short-Circuit Fault," Energies, MDPI, vol. 14(11), pages 1-16, May.
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