energies-12-01805.pdf - energies Article Modified Power Curves for Prediction of Power Output of Wind Farms Mohsen Vahidzadeh 1,2 and Corey D Markfort

energies-12-01805.pdf - energies Article Modified Power...

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energies Article Modified Power Curves for Prediction of Power Output of Wind Farms Mohsen Vahidzadeh 1,2 and Corey D. Markfort 1,2 * 1 IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA 52242, USA; [email protected] 2 Civil and Environmental Engineering, The University of Iowa, Iowa City, IA 52242, USA * Correspondence: [email protected]; Tel.: +1-319-335-6168 Received: 31 March 2019; Accepted: 6 May 2019; Published: 12 May 2019 Abstract: Power curves are used to model power generation of wind turbines, which in turn is used for wind energy assessment and forecasting total wind farm power output of operating wind farms. Power curves are based on ideal uniform inflow conditions, however, as wind turbines are installed in regions of heterogeneous and complex terrain, the effect of non-ideal operating conditions resulting in variability of the inflow must be considered. We propose an approach to include turbulence, yaw error, air density, wind veer and shear in the prediction of turbine power by using high resolution wind measurements. In this study, two modified power curves using standard ten-minute wind speed and high resolution one-second data along with a derived power surface were tested and compared to the standard operating curve for a 2.5 MW horizontal axis wind turbine. Data from supervisory control and data acquisition (SCADA) system along with wind speed measurements from a nacelle-mounted sonic anemometer and wind speed measurements from a nearby meteorological tower are used in the models. The results show that all of the proposed models perform better than the standard power curve while the power surface results in the most accurate power prediction. Keywords: atmospheric boundary layer; equivalent wind speed; power curve; turbulence; wind power prediction; meteorological tower 1. Introduction As renewable energy becomes more prevalent, its integration into the power grid and the prediction of its energy contribution to the grid becomes more important. In the case of wind energy, prediction of power production is even more challenging due to its dependence on not only wind speed, but also meteorological conditions such as turbulence, wind shear, wind veer and air density. One way to predict power generation is by using wind forecasts as input for wind power models, which in turn predict wind power generation based on atmospheric boundary layer data. A model that accurately converts atmospheric data to wind power would be a powerful tool for power generation forecasting. The model that is typically used for this purpose is a standard power curve. Usually provided by wind turbine manufacturers, it relates ten-minute hub height wind speed to wind turbine power. However, evidence shows that only relating power output to hub height wind speed yields inaccuracy in power prediction. Clifton et al. [ 1 ] showed that power production can deviate by 5–10% compared to manufacturer’s power curve predictions and attributed the error to not accounting for turbulence. Additionally, the miss-alignment between the rotor and wind direction (yaw error)
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