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Validation of various windmill brake state models used by blade element momentum calculation

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  • Pratumnopharat, P.
  • Leung, P.S.

Abstract

The concept of windmill brake state model is considered in this paper. Blade Element Momentum (BEM) calculation often calculates the value of thrust coefficient in windmill brake state. Unfortunately, thrust coefficient predicted by momentum theory deviated dramatically from the experimental data when the value of axial induction factor is greater than 0.5. To solve this problem and to increase the accuracy of the prediction, windmill break state model including tip loss effect must be applied to equations of thrust coefficient. The problem of interest is that which windmill break state model is suitable for the wind turbine model being simulated. The purpose of this paper is to compare the rotor power predicted by six different windmill brake state models. The aerodynamic code based on BEM theory has been implemented in Matlab and validated with the simulated result of AWT-27 wind turbine model reported by National Renewable Energy Laboratory (NREL). Six windmill brake state models to be compared are Glauert’s characteristic equation, classical brake state model, advanced brake state model, Wilson and Walker model, modified advanced brake state model, and Shen’s correction. The predicted power curves obtained from each windmill brake state model are compared to the measured power curve of AWT-27/P4. It has been shown that Shen’s correction gives the highest correlation to the measured data with r-square of 0.970 and the predicted annual energy production (AEP) is different from measured data by only 6.3%.

Suggested Citation

  • Pratumnopharat, P. & Leung, P.S., 2011. "Validation of various windmill brake state models used by blade element momentum calculation," Renewable Energy, Elsevier, vol. 36(11), pages 3222-3227.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:11:p:3222-3227
    DOI: 10.1016/j.renene.2011.03.027
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    References listed on IDEAS

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    1. Lanzafame, R. & Messina, M., 2010. "Power curve control in micro wind turbine design," Energy, Elsevier, vol. 35(2), pages 556-561.
    2. Mejía, Juan M. & Chejne, Farid & Smith, Ricardo & Rodríguez, Luis F. & Fernández, Oscar & Dyner, Isaac, 2006. "Simulation of wind energy output at Guajira, Colombia," Renewable Energy, Elsevier, vol. 31(3), pages 383-399.
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    1. Pratumnopharat, Panu & Leung, Pak Sing & Court, Richard S., 2014. "Wavelet transform-based stress-time history editing of horizontal axis wind turbine blades," Renewable Energy, Elsevier, vol. 63(C), pages 558-575.
    2. Pratumnopharat, Panu & Leung, Pak Sing & Court, Richard S., 2013. "Extracting fatigue damage parts from the stress–time history of horizontal axis wind turbine blades," Renewable Energy, Elsevier, vol. 58(C), pages 115-126.
    3. Peng, Yi-Xin & Xu, You-Lin & Zhan, Sheng, 2019. "A hybrid DMST model for pitch optimization and performance assessment of high-solidity straight-bladed vertical axis wind turbines," Applied Energy, Elsevier, vol. 250(C), pages 215-228.
    4. Lanzafame, R. & Messina, M., 2013. "Advanced brake state model and aerodynamic post-stall model for horizontal axis wind turbines," Renewable Energy, Elsevier, vol. 50(C), pages 415-420.
    5. Yong Ma & Aiming Zhang & Lele Yang & Chao Hu & Yue Bai, 2019. "Investigation on Optimization Design of Offshore Wind Turbine Blades based on Particle Swarm Optimization," Energies, MDPI, vol. 12(10), pages 1-18, May.
    6. Jaime Liew & Kirby S. Heck & Michael F. Howland, 2024. "Unified momentum model for rotor aerodynamics across operating regimes," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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