Using nacelle-based wind speed observations to improve power curve modeling for wind power forecasting

Wind Energy

Volumn 15, Issue 2, 2012, Pages 245-258

  Cutler, Nicholas J ^a   Outhred, Hugh R ^a   MacGill, Iain F ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

met mast observations; nacelle average wind speed measurements; nacelle based anemometry; NWP system; power curve modeling; wind power forecasting

Indexed keywords

ELECTRIC POWER SYSTEM INTERCONNECTION; ELECTRIC UTILITIES; SPEED; WEATHER FORECASTING; WIND; WIND POWER; WIND TURBINES;

MET MAST OBSERVATIONS; NACELLE-BASED ANEMOMETRY; NWP SYSTEM; POWER CURVES; WIND POWER FORECASTING; WIND SPEED MEASUREMENT;

FLOW VISUALIZATION;

ACCURACY ASSESSMENT; ANEMOMETER; CORRECTION; POWER GENERATION; REAL TIME; TIME SERIES; WIND DIRECTION; WIND FARM; WIND POWER; WIND VELOCITY;

AUSTRALIA;

EID: 84859099325     PISSN: 10954244     EISSN: 10991824     Source Type: Journal    
DOI: 10.1002/we.465     Document Type: Article

References (24)

1. Monteiro CA, Bessa R, Miranda V, Botterud A, Wang J, Conzelmann G,. Wind power forecasting: state-of-the-art 2009. Technical Report ANL/DIS-10-1, Argonne National Laboratory, U.S. Department of Energy, Argonne, Illinois, November 2006; 216. [Online]. Available: (Accessed 24 March 2011).
2. Thorncraft S, Outhred H, Clements D, Barker F,. Market-based ancillary services in the Australian National Electricity Market for increased levels of wind integration. Wind Engineering 2008; 32: 1-12. DOI: 10.1260/ 030952408784305868.
3. Ernst B, Oakleaf B, Ahlstrom M, Lange M, Möhrlen C, Lange B, Focken U, Rohrig K,. Predicting the wind. IEEE Power and Energy Magazine 2008; 5: 78-89. DOI: 10.1109/MPE2007.906306. (Pubitemid 350121780)
4. Grant W, Edelson D, Dumas J, Zack J, Ahlstrom M, Kehler J, Storck P, Lerner J, Parks K, Finley C,. Change in the air. IEEE Power and Energy Magazine 2009; 7: 47-58. DOI: 10.1109/MPE2009.934261.
5. Madsen H, Pinson P, Kariniotakis G, Nielsen HA, Nielsen TS,. Standardizing the performance evaluation of short-term wind power prediction models. Wind Engineering 2005; 29: 475-489. DOI: 10.1260/030952405776234599.
6. Larson KA, Westrick K,. Short-term wind forecasting using off-site observations. Wind Energy 2006; 9: 55-62. DOI: 10.1002/we.179. (Pubitemid 43281984)
7. Llombart A, Watson SJ, Llombart D, Fandos JM,. Power curve characterization I: improving the bin method. Proceedings of the International Conference of Renewable Energies and Power Quality (ICREPQ), Zaragoza, Spain, 16-18 March 2005.
8. Madsen H, Nielsen HA, Nielsen TS,. A tool for predicting wind power production of off-shore wind plants. Proceedings of Copenhagen Offshore Wind, Copenhagen, Denmark, 2005. Available on CD-ROM.
9. Potter CW, Fowler PC,. Non-deterministic wind speed to power output conversion. Proceedings of the Windpower 2008 Conference and Exhibition, Houston, Texas, 2008.
10. Smith B, Link H, Randall G, McCoy T,. Applicability of nacelle anemometer measurements for use in turbine power performance tests. Proceedings of American Wind Energy Association WINDPOWER Conference, Portland, Oregon, 2002. [Online]. Available:.
11. IEC (2005). Wind turbines-Part 12-1: power performance measurements of electricity producing wind turbines, IEC 61400-1-12, International Electrotechnical Commission.
12. Frandsen S, Sørensen JN, Mikkelsen R, Pedersen TF, Antoniou I, Hansen K,. The generics of wind turbine nacelle anemometry. roceedings of European Wind Energy Conference, Marseille, France, 2009. [Online]. Available:.
13. IEC (2008). Wind turbines-Part 12-2: power performance of electricity producing wind turbines based on nacelle aneometry, IEC 61400-1-12, International Electrotechnical Commission.
14. Antoniou I, Pedersen TF,. Nacelle Anemometry on a 1MW Wind Turbine: Comparing the power performance results by use of the nacelle or mast anemometer. Risø National Laboratory, Risø-R-941(EN), Roskilde, Denmark, 1997; 35. [Online]. Available
15. Smaili A, Masson C,. On the rotor effects upon nacelle anemometry for wind turbines. Wind Engineering 2004; 28: 695-714, DOI: 10.1260/ 0309524043729958. (Pubitemid 40518064)
16. Cabezon D, Marti I, San Isidro J, Perez I,. Comparison of methods for power curve modelling. Proceedings of Global Windpower, Chicago, Illinois, March 2004; 28-31.
17. Maggio D, D'Annunzio C, Huang SH, Thompson C,. Utilization of forecasts for wind-powered generation resources in ERCOT operations. Proceedings of the IEEE Power and Energy Society Conference, Minneapolis, Minnesota, July 2010; 25-29.
18. AEMO (2009). Australian Energy Market Operator. [Online]. Available: (Accessed 19 October 2009).
19. Daley R,. Atmospheric Data Analysis. Cambridge University Press: Cambridge, 1991.
20. Ohring G, Lord S, Derber J, Mitchell K, Ji M,. Applications of satellite remote sensing in numerical weather and climate prediction. Advances in Space Research 2002; 30: 2433-2439. DOI: 10.1016/S0273-1177(02)80298-8. (Pubitemid 36386676)
21. Benjamin SG, Dévényi D, Weygandt SS, Brundage KJ, Brown JM, Grell GA, Kim D, Schwartz BE, Smirnova TG, Smith TL, Manikin GS,. An hourly assimilation-forecast cycle: the RUC. Monthly Weather Review 2004; 132: 495-518. DOI: 10.1175/1520-0493. (Pubitemid 38335447)
22. Draxl C, Vincent CL, Hahmann AN, Giebel G, Pinson P,. Variability forecasts for wind farms using high resolution initial conditions. 8th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Farms, Bremen, Germany, 2009.
23. Cutler NJ,. Characterising the uncertainty in potential large rapid changes in wind power generation. PhD Thesis, School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, 2009; 324.
24. Vincent C, Bourke W, Kepert JD, Chattopadhyay M, Ma Y, Steinle PJ, Tingwell CIW,. Verification of a high-resolution mesoscale NWP system. Australian Meteorological Magazine 2008; 57: 213-233.