Determining wind turbine power curves based on operating conditions

Authors

  • L.T. Paiva,

    1. CEsA—Research Centre for Wind Energy and Atmospheric Flows, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
    Search for more papers by this author
  • C. Veiga Rodrigues,

    1. CEsA—Research Centre for Wind Energy and Atmospheric Flows, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
    Search for more papers by this author
  • J.M.L.M. Palma

    Corresponding author
    1. CEsA—Research Centre for Wind Energy and Atmospheric Flows, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
    • Correspondence: J. M. L. M. Palma, CEsA—Research Centre for Wind Energy and Atmospheric Flows, Faculty of Engineering University of Porto Rua Dr Roberto Frias s/n, 4200-465 Porto Portugal

      E-mail: jpalma@fe.up.pt

    Search for more papers by this author

ABSTRACT

The relation between wind speed and electrical power—the power curve—is essential in the design, management and power forecasting of a wind farm. The power curve is the main characteristic of a wind turbine, and a procedure is presented for its determination, after the wind turbine is installed and in operation. The procedure is based on both computational and statistical techniques, in situ measurements, nacelle anemometry and operational data. This can be an alternative or a complement to procedures fully based on field measurements as in the International Electrotechnical Commission standards, reducing the time and costs of such practices. The impact of a more accurate power curve was measured in terms of the prediction error of a wind power forecasting system over 1 year of operation, whereby the methodology for numerical site calibration was presented and the concepts of ideal power curve and nacelle power curve introduced. The validation was based on data from wind turbines installed at a wind farm in complex topography, in Portugal, providing a real test of the technique presented here. The contribution of the power curve to the wind power forecasting uncertainty was found to be from 10% to 15% of the root mean square error. Copyright © 2013 John Wiley & Sons, Ltd.

Ancillary