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Wind Turbine Power Maximization Using Log-Power Proportional-Integral Extremum Seeking

Author

Listed:
  • Devesh Kumar

    (Department of Electrical and Computer Engineering and Center for Wind Energy, The University of Texas at Dallas, Richardson, TX 75080, USA)

  • Mario A. Rotea

    (Department of Mechanical Engineering and Center for Wind Energy, The University of Texas at Dallas, Richardson, TX 75080, USA)

Abstract

This paper proposes a Log-Power Proportional-Integral Extremum Seeking Control (LP-PIESC) framework for maximizing the power capture of a wind turbine operating at below-rated wind speeds, i.e., the so-called region-2 of a turbine’s power curve. Extremum seeking control (ESC) has emerged as a viable algorithm to maximize energy capture for a wind turbine operating in region-2. Despite the encouraging results of early ESC strategies, the basic algorithm suffers from slow and inconsistent convergence behavior under changing wind speed within region-2. It has been shown that replacing the power signal with its logarithm results in an algorithm that is robust and predictable even when the mean wind speed varies. In addition, new studies have suggested that replacing conventional ESC with proportional plus integral ESC (PIESC) results in faster convergence to optimal conditions. In the current paper, the idea of log-power feedback is merged with the PIESC scheme and is applied to tune the parameters of the region-2 torque controller for the NREL 5-MW turbine reference model. The results of this new algorithm are compared with the ESC with log-of-power feedback using NREL OpenFAST simulations. The log-power feedback PIESC is also implemented for the blade pitch set-point angle. Energy capture over the course of the simulations and damage equivalent loads calculated with MLife are used to assess the results. The simulations performed under different turbulent intensity cases demonstrate the rapid convergence of the log-power feedback PIESC.

Suggested Citation

  • Devesh Kumar & Mario A. Rotea, 2022. "Wind Turbine Power Maximization Using Log-Power Proportional-Integral Extremum Seeking," Energies, MDPI, vol. 15(3), pages 1-24, January.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1004-:d:737794
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    Cited by:

    1. Aditya H. Bhatt & Mireille Rodrigues & Federico Bernardoni & Stefano Leonardi & Armin Zare, 2023. "Stochastic Dynamical Modeling of Wind Farm Turbulence," Energies, MDPI, vol. 16(19), pages 1-24, September.

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