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EWEC 2009: Modelling wind flow 19 March 2009 qua

EWEC 2009: Modelling wind flow 19 March 2009 qua. Modelling the risk of icing. Dr. Silke Dierer 1 René Cattin 1 Dr. Alain Heimo 1 Bj ørn Egil Nygaard 2 Kristiina Säntti 3 1 METEOTEST, Switzerland 2 Norwegian Meteorological Institute, Norway 3 Finnish Meteorological Institute, Finland.

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EWEC 2009: Modelling wind flow 19 March 2009 qua

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  1. EWEC 2009: Modelling wind flow 19 March 2009qua Modelling the risk of icing Dr. Silke Dierer1 René Cattin1 Dr. Alain Heimo1 Bjørn Egil Nygaard2 Kristiina Säntti3 1METEOTEST, Switzerland 2 Norwegian Meteorological Institute, Norway 3 Finnish Meteorological Institute, Finland

  2. Icing and wind energy • Icing interferes with aerodynamics of blades => reduced power production. • Icing causes unbalanced mass => faster fatigue of material. • Icing might cause ice throw => safety risk • Thus, knowledge about icing is important: • for planning = icing maps • during operation = forecasts T. Wallenius: The effect of Icing on energy production losses of wind turbines with different control strategies, EWEC 2008

  3. Risk of icing in Europe: icing days per year 8 – 14 days/year 15 - 30 days/year > 30 days/year No icing < 1 day/year 2 -7 days/year T. Laakso, H. Holttinen, G. Ronsten, L.Tallhaug, R.Horbaty, I. Baring-Gould, A. Lacroix, E. Peltola, B. Tammelin, 2005: State-of-the-art of wind energy in cold climate, IEA Wind Annex XIX, 53 p. http:\arcticwind.vtt.fi, date of access 12.3.2009 Source: Tammelin, B., Cavaliere, M., Holttinen, H., Morgan, C., Seifert, H., Säntti, K., Wind Energy Production in Cold Climate, Meteorological Publications No. 41, Finnish Meteorological Institute, Helsinki. 2000.

  4. COST-727 “Measuring and forecasting atmospheric icing on structures” • Aim: • improved understanding of in-cloud icing, wet snow and freezing rain in different European regions • enhance the potential to observe, monitor and forecast icing • Method: • In-situ icing measurements at six stations in different regions of Europe • Development and evaluation of icing models Measurements • Aim of the current study: • Coupling a weather model with an icing algorithm and test for different regions • improve method for icing map calculation • Evaluate the potential of icing forecasts Modelling

  5. Overview • Method and models • Results Luosto, Finland • Results Gütsch, Switzerland • Icing forecasts Schwyberg, Switzerland • Summary

  6. Method and models

  7. Wind, temperature, cloud and rain water Simulated ice load Model system for icing simulations Algorithm to calculate icing on structures (Makkonen, 2000)

  8. Mesoscale weather model WRF • Up-to-date mesoscale atmospheric model for: • Operational weather forecasts • Research purposes • Application range: • Starting from large eddy simulations: Δx = 100m • Up to regional climate simulations: Δx = 100km

  9. Algorithm for calculating icing on structures (Makkonen, 2000) • Input: • cloud water content • cloud droplet concentration • wind • temperature • Output: • ice mass • Model by Makkonen (2000) calculates ice load on a cylinder caused by cloud droplets accretion

  10. Results Luosto, Finland

  11. Luosto, 23 – 25 December 2007:

  12. Luosto, 21 – 25 December 2007: time series of measured wind, temperature and ice load • WRF simulation at 800m grid size • ECMWF data as initial and boundary data • Cloud droplet number concentration: est. 75 1/cm3

  13. Luosto, 23 – 25 December 2007: time series of measured and simulated ice load WRF simulation at 2.4 km grid size WRF simulation at 0.8 km grid size

  14. Results Gütsch, Switzerland

  15. Positions of icing measurements in Switzerland Prevailing wind direction 22 – 24 November 2007

  16. Wind direction North South Position of Gütsch site Gütsch, 23 November 2007, 08 UTC: vertical cross section of hydrometeors

  17. Gütsch, 22 - 24 November 2007: time series of simulated ice load - WRF at Δx = 2.4 and 0.8 km WRF simulation, Δx=2.4 km WRF simulation, Δx=0.8 km Maximum ice load 0.0 kg/m Maximum ice load 1.3 kg/m

  18. Gütsch, 22 - 24 November 2007: sensitivity regarding droplet number concentration WRF, Δx=800m, Nd = 70 1/cm3 WRF, Δx=800m, Nd = 35 1/cm3 Maximum ice load 1.3 kg/m Maximum ice load 0.9 kg/m

  19. Positions of icing measurements in Switzerland

  20. Icing forecasts for Schwyberg, Switzerland using the COSMO model Schwyberg, 21.11.2008 Schwyberg, 12.11.2008 Ice load is simulated driving the Makkonen model with results of the Swiss operational weather forecast model COSMO-2 at 2.2 km grid size Schwyberg, 11.12.2008

  21. Summary • Good results regarding • capturing icing events • the timing of icing events • Quantitative forecast of ice load less precise • Accuracy of measurements uncertain • Difficulties to define the most suitable grid box • Strong sensitivity regarding horizontal resolution and cloud droplet concentration • First results of icing forecasts for Switzerland indicate that there is a potential to forecast icing events

  22. Thank you for your attention

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