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A methodology to develop reduced-order models to support the operation and maintenance of offshore wind turbines

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  • Lin, Zi
  • Cevasco, Debora
  • Collu, Maurizio

Abstract

From an operation & maintenance (O&M) point of view, it is necessary to model the aero-hydro-servo-elastic (AHSE) dynamics of each wind turbine but, on the other side, wind farms generally include hundreds of wind turbines. Simply using and linking several advanced, single wind turbine models of dynamics to represent a wind farm can be computationally prohibitive. To this end, this paper developed a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure, having a lower computational cost and therefore more easily scalable up to a wind farm level. First, a nonlinear AHSE model is used to derive the time-domain response of the wind turbine degrees of freedom (DOFs). The failure mode, its relevant DOF, and the relevant operational conditions during which the failure is likely to occur are identified. A linearisation of the nonlinear aero-hydro-servo-elastic-drivetrain (AHSE-DT) model is then carried out. Subsequently, a number of linear ROMs are developed based on the linear full-order system but excluding high-frequency states using the modal truncation (MT) method. For the targeted DOF (rotor torque signal) and the load cases simulated, the results from the linear ROMs showed that the blade modes are important to capture not only the DOF of extreme values, but also the DOF of high-frequency responses (above 1.5 Hz). The results from nonlinear ROMs showed that the ROM eliminating all the tower modes (rigid tower) is acceptable to capture the DOF of low-frequency response (below 0.5 Hz), as it has almost the same spectral responses as the full-order nonlinear model.

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  • Lin, Zi & Cevasco, Debora & Collu, Maurizio, 2020. "A methodology to develop reduced-order models to support the operation and maintenance of offshore wind turbines," Applied Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:appene:v:259:y:2020:i:c:s0306261919319154
    DOI: 10.1016/j.apenergy.2019.114228
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    References listed on IDEAS

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    Cited by:

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    3. Liu, Min & Qin, Jianjun & Lu, Da-Gang & Zhang, Wei-Heng & Zhu, Jiang-Sheng & Faber, Michael Havbro, 2022. "Towards resilience of offshore wind farms: A framework and application to asset integrity management," Applied Energy, Elsevier, vol. 322(C).
    4. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Damage identification of wind turbine blades using an adaptive method for compressive beamforming based on the generalized minimax-concave penalty function," Renewable Energy, Elsevier, vol. 181(C), pages 59-70.
    5. Cheng Yang & Jun Jia & Ke He & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey," Energies, MDPI, vol. 16(14), pages 1-39, July.
    6. Vincent F. Yu & Thi Huynh Anh Le & Tai-Sheng Su & Shih-Wei Lin, 2021. "Optimal Maintenance Policy for Offshore Wind Systems," Energies, MDPI, vol. 14(19), pages 1-19, September.
    7. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy," Applied Energy, Elsevier, vol. 313(C).
    8. Zhao Song & Christoph M. Hackl & Abhinav Anand & Andre Thommessen & Jonas Petzschmann & Omar Kamel & Robert Braunbehrens & Anton Kaifel & Christian Roos & Stefan Hauptmann, 2023. "Digital Twins for the Future Power System: An Overview and a Future Perspective," Sustainability, MDPI, vol. 15(6), pages 1-29, March.

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