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Load monitoring for active control of wind turbines

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  • Cooperman, Aubryn
  • Martinez, Marcias

Abstract

This review article examines the range of sensors that have been proposed for monitoring wind turbine blade loads for the purpose of active load control over the past decade. Wind turbine active load control requires sensors that are able to detect loads as they occur, in order to enable a prompt actuation of control devices. Loads may be detected based on structural effects or inferred from aerodynamic measurements. This paper is organized into the following sections: wind turbine control, requirements for load monitoring sensors, sensing technologies and field tests of load control. The types of sensors examined in this article include fiber optic sensors, inertial sensors, pressure measurements and remote optical sensing.

Suggested Citation

  • Cooperman, Aubryn & Martinez, Marcias, 2015. "Load monitoring for active control of wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 189-201.
    • Handle: RePEc:eee:rensus:v:41:y:2015:i:c:p:189-201
    DOI: 10.1016/j.rser.2014.08.029
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    References listed on IDEAS

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    1. Schubel, P.J. & Crossley, R.J. & Boateng, E.K.G. & Hutchinson, J.R., 2013. "Review of structural health and cure monitoring techniques for large wind turbine blades," Renewable Energy, Elsevier, vol. 51(C), pages 113-123.
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    Cited by:

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    4. Zhang, Mingming & Tan, Bin & Xu, Jianzhong, 2016. "Smart fatigue load control on the large-scale wind turbine blades using different sensing signals," Renewable Energy, Elsevier, vol. 87(P1), pages 111-119.
    5. Stephanie Ordonez-Sanchez & Matthew Allmark & Kate Porter & Robert Ellis & Catherine Lloyd & Ivan Santic & Tim O’Doherty & Cameron Johnstone, 2019. "Analysis of a Horizontal-Axis Tidal Turbine Performance in the Presence of Regular and Irregular Waves Using Two Control Strategies," Energies, MDPI, vol. 12(3), pages 1-22, January.
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    7. Majidi Nezhad, M. & Heydari, A. & Groppi, D. & Cumo, F. & Astiaso Garcia, D., 2020. "Wind source potential assessment using Sentinel 1 satellite and a new forecasting model based on machine learning: A case study Sardinia islands," Renewable Energy, Elsevier, vol. 155(C), pages 212-224.
    8. Mousavi, Yashar & Bevan, Geraint & Kucukdemiral, Ibrahim Beklan & Fekih, Afef, 2022. "Sliding mode control of wind energy conversion systems: Trends and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    9. Bakdi, Azzeddine & Kouadri, Abdelmalek & Mekhilef, Saad, 2019. "A data-driven algorithm for online detection of component and system faults in modern wind turbines at different operating zones," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 546-555.

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