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Estimation of blade forces in wind turbines using blade root strain measurements with OpenFAST verification

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  • Moynihan, Bridget
  • Moaveni, Babak
  • Liberatore, Sauro
  • Hines, Eric

Abstract

This paper introduces an inference method for computing the forces and bending moments on operating wind turbine blades using strain measurements and supervisory control and data acquisition (SCADA) data. Operational data from four months of a Clipper Liberty C96 2.5 MW turbine instrumented with interferometric strain sensors at the blade roots as well as SCADA data such as wind speed, rotor hub speed, and blade pitch angle allow for accurate calculation of blade forces and moments. To perform such calculations, certain structural properties of the turbine blades must be inferred in the absence of detailed, proprietary information. This is done by inferring missing information from the National Renewable Energy Laboratory (NREL) 3 MW WindPACT reference wind turbine specifications. The derived forces and moments computed on the blades of the Clipper turbine are compared with the behavior of the NREL 3 MW reference turbine according to OpenFAST simulation outputs. Comparison of blade root reaction forces to OpenFAST outputs match closely, demonstrating that this inference method can be used to successfully estimate the internal forces and bending moments acting on the blades. These methods are useful on turbines for which all the structural information is not available.

Suggested Citation

  • Moynihan, Bridget & Moaveni, Babak & Liberatore, Sauro & Hines, Eric, 2022. "Estimation of blade forces in wind turbines using blade root strain measurements with OpenFAST verification," Renewable Energy, Elsevier, vol. 184(C), pages 662-676.
    • Handle: RePEc:eee:renene:v:184:y:2022:i:c:p:662-676
    DOI: 10.1016/j.renene.2021.11.094
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    References listed on IDEAS

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    1. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
    2. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
    3. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
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    Cited by:

    1. Wang, Bingkai & Sun, Wenlei & Wang, Hongwei & Xu, Tiantian & Zou, Yi, 2024. "Research on rapid calculation method of wind turbine blade strain for digital twin," Renewable Energy, Elsevier, vol. 221(C).
    2. Hines, Eric M. & Baxter, Christopher D.P. & Ciochetto, David & Song, Mingming & Sparrevik, Per & Meland, Henrik J. & Strout, James M. & Bradshaw, Aaron & Hu, Sau-Lon & Basurto, Jorge R. & Moaveni, Bab, 2023. "Structural instrumentation and monitoring of the Block Island Offshore Wind Farm," Renewable Energy, Elsevier, vol. 202(C), pages 1032-1045.

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