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Automatic identification of wind turbine models using evolutionary multiobjective optimization

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  • La Cava, William
  • Danai, Kourosh
  • Spector, Lee
  • Fleming, Paul
  • Wright, Alan
  • Lackner, Matthew

Abstract

Modern industrial-scale wind turbines are nonlinear systems that operate in turbulent environments. As such, it is difficult to characterize their behavior accurately across a wide range of operating conditions using physically meaningful models. Customarily, the models derived from wind turbine data are in ‘black box’ format, lacking in both conciseness and intelligibility. To address these deficiencies, we use a recently developed symbolic regression method to identify models of a modern horizontal-axis wind turbine in symbolic form. The method uses evolutionary multiobjective optimization to produce succinct dynamic models from operational data while making minimal assumptions about the physical properties of the system. We compare the models produced by this method to models derived by other methods according to their estimation capacity and evaluate the trade-off between model intelligibility and accuracy. Several succinct models are found that predict wind turbine behavior as well as or better than more complex alternatives derived by other methods. We interpret the new models to show that they often contain intelligible estimates of real process physics.

Suggested Citation

  • La Cava, William & Danai, Kourosh & Spector, Lee & Fleming, Paul & Wright, Alan & Lackner, Matthew, 2016. "Automatic identification of wind turbine models using evolutionary multiobjective optimization," Renewable Energy, Elsevier, vol. 87(P2), pages 892-902.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p2:p:892-902
    DOI: 10.1016/j.renene.2015.09.068
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    References listed on IDEAS

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    Cited by:

    1. Dai, Juchuan & Tan, Yayi & Shen, Xiangbin, 2019. "Investigation of energy output in mountain wind farm using multiple-units SCADA data," Applied Energy, Elsevier, vol. 239(C), pages 225-238.
    2. Peng, Chao & Zou, Jianxiao & Li, Yan & Xu, Hongbing & Li, Liying, 2017. "A novel composite calculation model for power coefficient and flapping moment coefficient of wind turbine," Energy, Elsevier, vol. 126(C), pages 821-829.
    3. Jingchun Chu & Ling Yuan & Yang Hu & Chenyang Pan & Lei Pan, 2019. "Comparative Analysis of Identification Methods for Mechanical Dynamics of Large-Scale Wind Turbine," Energies, MDPI, vol. 12(18), pages 1-24, September.

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