Alternative Calibration of Cup Anemometers: A Way to Reduce the Uncertainty of Wind Power Density Estimation

This study presents a procedure to reduce the uncertainty of wind power density estimations, which is useful to improve the energy production predictions of wind farms. Power density is usually determined from the wind speed measured by a cup anemometer and the air density value (conventional procedure). An alternative procedure based on wind speed and dynamic pressure estimations provided by a cup anemometer is proposed. The dynamic pressure is obtained by means of a calibration curve that relates the anemometer rotation frequency and the dynamic pressure measured by a Pitot tube. The quadratic regression, used to define the calibration curve, and its uncertainty are both detailed. A comparison between the alternative procedure and the conventional one points out the advantage of the proposed alternative since results show a high reduction of the indirect measurement uncertainty of wind power density.

[1]  Global Energy Assessment Writing Team.  Global Energy Assessment: Toward a Sustainable Future , 2012 .

[2]  N. Nakicenovic,et al.  Global Energy Assessment – Toward a Sustainable Future , 2012 .

[3]  Santiago Pindado,et al.  Design and Development of a 5-Channel Arduino-Based Data Acquisition System (ABDAS) for Experimental Aerodynamics Research , 2018, Sensors.

[4]  D Brynn Hibbert,et al.  The uncertainty of a result from a linear calibration. , 2006, The Analyst.

[5]  Hugh Hamell,et al.  On a new anemometer , 1832 .

[6]  Anderson Rodrigo de Queiroz,et al.  Analysis of the wind average speed in different Brazilian states using the nested GR&R measurement system , 2018 .

[7]  Leif Kristensen,et al.  Cup Anemometer Behavior in Turbulent Environments , 1998 .

[8]  Kenichi Fujii,et al.  Revised formula for the density of moist air (CIPM-2007) , 2008 .

[9]  Javier Bajo,et al.  On Cup Anemometer Rotor Aerodynamics , 2012, Sensors.

[10]  Yoshiya Terao,et al.  Final report on the CIPM air speed key comparison (CCM.FF-K3) , 2007 .

[11]  S. Tavoularis.  Measurement in fluid mechanics , 2005 .

[12]  Javier Cubas,et al.  Improved analytical method to study the cup anemometer performance , 2015 .

[13]  Jens Carsten Hansen,et al.  Field calibration of cup anemometers , 2007 .

[14]  Ali Mostafaeipour,et al.  Determination of rated wind speed for maximum annual energy production of variable speed wind turbines , 2017 .

[15]  Jovan Isaković,et al.  Cascade nonlinear feedforward-feedback control of stagnation pressure in a supersonic blowdown wind tunnel , 2017 .

[16]  José M. Matías,et al.  Performance assessment of five MCP models proposed for the estimation of long-term wind turbine power outputs at a target site using three machine learning techniques , 2018 .

[17]  Ping Ju,et al.  Equivalent modeling of wind energy conversion considering overall effect of pitch angle controllers in wind farm , 2018, Applied Energy.

[18]  Kenichi Fujii,et al.  Considerations on future redefinitions of the kilogram, the mole and of other units , 2007 .

[19]  Santiago Pindado,et al.  Analysis of calibration results from cup and propeller anemometers. Influence on wind turbine Annual Energy Production (AEP) calculations , 2011 .

[20]  Javier Cubas,et al.  The Cup Anemometer, a Fundamental Meteorological Instrument for the Wind Energy Industry , 2014, ECSA 2014.

[21]  Arnold Wexler,et al.  Humidity and moisture : measurement and control in science and industry , 1965 .

[22]  Félix Sorribes-Palmer,et al.  A procedure for calibrating the spinning ultrasonic wind sensors , 2018 .

[23]  Allan J. Zuckerwar,et al.  Low‐frequency absorption of sound in air , 1985 .

[24]  Javier Cubas,et al.  The Cup Anemometer, a Fundamental Meteorological Instrument for the Wind Energy Industry. Research at the IDR/UPM Institute , 2014, Sensors.

[25]  J. Anderson,et al.  Fundamentals of Aerodynamics , 1984 .

[26]  Javier Cubas,et al.  Studies on Cup Anemometer Performances Carried out at IDR/UPM Institute. Past and Present Research , 2017 .

[27]  Svend Ole Hansen,et al.  Wind Tunnel Calibration of Cup Anemometers , 2012 .

[28]  Gabriel Ibarra-Berastegi,et al.  Seasonal Air Density Variations over The East of Scotland and The Consequences for Offshore Wind Energy , 2018, 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA).

[29]  Pawel Ligeza.  Model and Simulation Studies of the Method for Optimization of Dynamic Properties of Tachometric Anemometers , 2018, Sensors.

[30]  E. Iso,et al.  Measurement Uncertainty and Probability: Guide to the Expression of Uncertainty in Measurement , 1995 .

[31]  Scott Schreck,et al.  Wind turbine power production and annual energy production depend on atmospheric stability and turbulence , 2016 .