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Wind turbine fatigue reduction based on economic-tracking NMPC with direct ANN fatigue estimation

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  • Luna, Julio
  • Falkenberg, Ole
  • Gros, Sébastien
  • Schild, Axel

Abstract

The aim of this work is to deploy an advanced Nonlinear Model Predictive Control (NMPC) approach for reducing the tower fatigue of a wind turbine (WT) tower while guaranteeing efficient energy extraction from the wind. To achieve this, different Artificial Neural Network (ANN) architectures are trained and tested in order to estimate the tower fatigue as a surrogate of the traditional Rainflow Counting (RFC) method. The ANNs receive data stemming from the tower top oscillation velocity and the previous fatigue state to directly estimate the fatigue progression. The results are compared to select the most convenient architecture for control implementation. Once an ANN is selected, an economic-tracking NMPC (etNMPC) solution to reduce the fatigue of the WT tower is deployed in real-time. The closed-loop results are then compared to a baseline controller from a renowned WT simulation tool and a classic etNMPC implementation with indirect fatigue minimisation to demonstrate the improvement achieved with the proposed strategy. Finally, conclusions regarding computational cost and real-time deployment capabilities are discussed, as well as future lines of research.

Suggested Citation

  • Luna, Julio & Falkenberg, Ole & Gros, Sébastien & Schild, Axel, 2020. "Wind turbine fatigue reduction based on economic-tracking NMPC with direct ANN fatigue estimation," Renewable Energy, Elsevier, vol. 147(P1), pages 1632-1641.
    • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:1632-1641
    DOI: 10.1016/j.renene.2019.09.092
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    References listed on IDEAS

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    1. Sichilalu, Sam & Mathaba, Tebello & Xia, Xiaohua, 2017. "Optimal control of a wind–PV-hybrid powered heat pump water heater," Applied Energy, Elsevier, vol. 185(P2), pages 1173-1184.
    2. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
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    Cited by:

    1. Liu, Zhenqing & Wang, Yize & Nyangi, Patrice & Zhu, Zhiwen & Hua, Xugang, 2021. "Proposal of a novel GPU-accelerated lifetime optimization method for onshore wind turbine dampers under real wind distribution," Renewable Energy, Elsevier, vol. 168(C), pages 516-543.
    2. Do, M. Hung & Söffker, Dirk, 2021. "State-of-the-art in integrated prognostics and health management control for utility-scale wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    3. He, Ruiyang & Yang, Hongxing & Sun, Shilin & Lu, Lin & Sun, Haiying & Gao, Xiaoxia, 2022. "A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control," Applied Energy, Elsevier, vol. 326(C).
    4. Zhang, Lijun & Miao, Junjie & Gu, Jiawei & Li, Xiang & Hu, Kuoliang & Zhu, Huaibao & Sun, Xuefa & Liu, Jing & Liu, Yanxin & Wang, Zhiwei, 2021. "A method of reducing the radial load of the shaft of a vertical axis wind turbine based on movable mass blocks," Renewable Energy, Elsevier, vol. 175(C), pages 952-964.
    5. He, Ruiyang & Yang, Hongxing & Lu, Lin, 2023. "Optimal yaw strategy and fatigue analysis of wind turbines under the combined effects of wake and yaw control," Applied Energy, Elsevier, vol. 337(C).
    6. Ren, Chao & Xing, Yihan, 2023. "AK-MDAmax: Maximum fatigue damage assessment of wind turbine towers considering multi-location with an active learning approach," Renewable Energy, Elsevier, vol. 215(C).

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