Coupled Atmospheric–Ice Load Model for Evaluation of Wind Plant Power Loss

Jing Yang Meteorological Research Division, Environment Canada, Dorval, Québec, Canada

Search for other papers by Jing Yang in
Current site
Google Scholar
PubMed
Close
,
Wei Yu Meteorological Research Division, Environment Canada, Dorval, Québec, Canada

Search for other papers by Wei Yu in
Current site
Google Scholar
PubMed
Close
,
Julien Choisnard Hydro Québec, Montreal, Québec, Canada

Search for other papers by Julien Choisnard in
Current site
Google Scholar
PubMed
Close
,
Alain Forcione Hydro-Québec Research Institute (IREQ), Varennes, Québec, Canada

Search for other papers by Alain Forcione in
Current site
Google Scholar
PubMed
Close
, and
Slavica Antic Hydro Québec, Montreal, Québec, Canada

Search for other papers by Slavica Antic in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Icing is a weather phenomenon that is typical of cold climates. It impacts human activities through ice accretion on tower structures, transmission lines, and the blades of wind turbines. Icing on turbine blades, in particular, results in wind turbine performance degradation and/or safety shutdowns. The objective of this study is to explore the feasibility of using a coupled atmospheric and ice load model to simulate icing start-up, duration, and amount while also quantitatively evaluating power loss in wind plants related to icing events and mechanisms. Eight of 27 icing episodes identified for a wind plant in the Gaspé region of Québec (Canada) during the period 2008–10 were simulated using a mesoscale model (the Global Environmental Multiscale Limited-Area Model, or GEM-LAM). The simulations were verified using near-surface temperature, relative humidity, and wind speed, all of which compared well to in situ observations. Simulated wind speed, precipitation, cloud liquid water content, and median volume diameter of the droplets were used to drive ice load models to simulate the total ice load on a cylindrical structure. The three ice load models accounted for freezing rain, wet snow, and in-cloud icing, respectively, and in all three cases a sink term was added to account for melting due to radiation. The start-up and duration of ice were well captured by the coupled model, and a positive correlation was found between icing episodes and wind power reduction. This study demonstrates the improvements of the icing forecasts by using three ice load models, and provides a framework for both qualitative and quantitative evaluation of icing impact on wind turbine operations.

Corresponding author address: Jing Yang, Atmospheric Numerical Prediction Research Section, Environment Canada, 2121 Transcanada Highway, No. 500, Dorval, QC H9P 1J3, Canada. E-mail: jing.yang@ec.gc.ca

Abstract

Icing is a weather phenomenon that is typical of cold climates. It impacts human activities through ice accretion on tower structures, transmission lines, and the blades of wind turbines. Icing on turbine blades, in particular, results in wind turbine performance degradation and/or safety shutdowns. The objective of this study is to explore the feasibility of using a coupled atmospheric and ice load model to simulate icing start-up, duration, and amount while also quantitatively evaluating power loss in wind plants related to icing events and mechanisms. Eight of 27 icing episodes identified for a wind plant in the Gaspé region of Québec (Canada) during the period 2008–10 were simulated using a mesoscale model (the Global Environmental Multiscale Limited-Area Model, or GEM-LAM). The simulations were verified using near-surface temperature, relative humidity, and wind speed, all of which compared well to in situ observations. Simulated wind speed, precipitation, cloud liquid water content, and median volume diameter of the droplets were used to drive ice load models to simulate the total ice load on a cylindrical structure. The three ice load models accounted for freezing rain, wet snow, and in-cloud icing, respectively, and in all three cases a sink term was added to account for melting due to radiation. The start-up and duration of ice were well captured by the coupled model, and a positive correlation was found between icing episodes and wind power reduction. This study demonstrates the improvements of the icing forecasts by using three ice load models, and provides a framework for both qualitative and quantitative evaluation of icing impact on wind turbine operations.

Corresponding author address: Jing Yang, Atmospheric Numerical Prediction Research Section, Environment Canada, 2121 Transcanada Highway, No. 500, Dorval, QC H9P 1J3, Canada. E-mail: jing.yang@ec.gc.ca
Save
  • Bélair, S., J. Mailhot, C. Girard, and P. Vaillancourt, 2005: Boundary layer and shallow cumulus clouds in a medium-range forecast of a large-scale weather system. Mon. Wea. Rev., 133, 19381960, doi:10.1175/MWR2958.1.

    • Search Google Scholar
    • Export Citation
  • Benoit, R., J. Côté, and J. Mailhot, 1989: Inclusion of a TKE boundary-layer parameterization in the Canadian regional finite-element model. Mon. Wea. Rev., 117, 17261750, doi:10.1175/1520-0493(1989)117<1726:IOATBL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bernstein, B. C., J. Hirvonen, E. Gregow, and I. Wittmeyer, 2012: Experiences from real-time LAPS-LOWICE runs over Sweden: 2011–2012 icing season. Winterwind Int. Wind Energy Conf., Skellefteå, Sweden, Swedish Wind Power Association, 21 pp. [Available online at http://www.slideshare.net/WinterwindConference/3a-bernstein-lapslowice.]

  • Byrkjedal, O., 2012a: Mapping of icing in Sweden—On the influence from icing on wind energy production. Winterwind Int. Wind Energy Conf., Skellefteå, Sweden, Swedish Wind Power Association, 20 pp. [Available online at http://www.slideshare.net/WinterwindConference/3a-byrkjedal-icingkvt.]

  • Byrkjedal, O., 2012b: The benefits of forecasting icing on wind energy production. Winterwind Int. Wind Energy Conf., Skellefteå, Sweden, Swedish Wind Power Association, 24 pp. [Available online at http://www.slideshare.net/WinterwindConference/3-forecast-kvt.]

  • Cattin, R., A. Heimo, S. Kunz, G. Russi, M. Russi, M. Tiefgraber, S. Schaffner, and B. E. Nygaard, 2007: Alpine Test Site Guetsch: Meteorological measurements and wind turbine performance analysis. Proc. 12th Int. Workshop on Atmospheric Icing of Structures (IWAIS), Yokohama, Japan, IWAIS, 6 pp. [Available online at http://www.meteotest.ch/cost727/media/IWAIS2007_paper.pdf.]

  • Chaine, P. M., and G. Castonguay, 1974: New Approach to Radial Ice Thickness Concept Applied to Bundle Like Conductors. Vol. 4, Industrial Meteorology, Environment Canada, 22 pp.

  • Choisnard, J., S. Antic, J. Bourrent, and C. Brabant, 2012: Centralized wind power forecasting system in Québec: Operational products in cold climate environment. Proc. EWEA Annual Event, Copenhagen, Denmark, European Wind Energy Association. [Available online at http://proceedings.ewea.org/annual2012/proceedings/ewec.php?id=571.]

  • Cortinas, J., Jr., 2000: A climatology of freezing rain in the Great Lakes region of North America. Mon. Wea. Rev., 128, 35743588, doi:10.1175/1520-0493(2001)129<3574:ACOFRI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Côté, J., S. Gravel, A. Méthot, A. Patoine, M. Roch, and A. Staniforth, 1998a: The operational CMC–MRB Global Environmental Multiscale (GEM) model. Part I: Design considerations and formulation. Mon. Wea. Rev., 126, 13731395, doi:10.1175/1520-0493(1998)126<1373:TOCMGE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Côté, J., J.-G. Desmarais, S. Gravel, A. Méthot, A. Patoine, M. Roch, and A. Staniforth, 1998b: The operational CMC–MRB Global Environmental Multiscale (GEM) model. Part II: Results. Mon. Wea. Rev., 126, 13971418, doi:10.1175/1520-0493(1998)126<1397:TOCMGE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Dalle, B., and P. Admirat, 2011: Wet snow accretion on overhead lines with French report of experience. Cold Reg. Sci. Technol., 65, 4351, doi:10.1016/j.coldregions.2010.04.015.

    • Search Google Scholar
    • Export Citation
  • Davis, N., A. N. Hahmann, N.-E. Clausen, and M. Žagar, 2014: Forecast of icing events at a wind farm in Sweden. J. Appl. Meteor. Climatol., 53, 262281, doi:10.1175/JAMC-D-13-09.1.

    • Search Google Scholar
    • Export Citation
  • Dierer, S., R. Cattin, S. C. Müller, B. E. K. Nygaard, P. Steiner, and B. Calpini, 2009: Modeling the risk of icing in Switzerland. Proc. 13th Int. Workshop on Atmospheric Icing of Structures (IWAIS), Andermatt, Switzerland, IWAIS, 7 pp. [Available online at http://iwais.compusult.net/html/IWAIS_Proceedings/IWAIS_2009/Session_3_cost_727_wg1/session_3_dierer.pdf.]

  • Dierer, S., R. Oechslin, and R. Cattin, 2011: Wind turbines in icing conditions: Performance and prediction. Adv. Sci. Res., 6, 245250, doi:10.5194/asr-6-245-2011.

    • Search Google Scholar
    • Export Citation
  • Drage, M. A., and G. Hauge, 2008: Atmospheric icing in a coastal mountainous terrain. Measurements and numerical simulations, a case study. Cold Reg. Sci. Technol., 53, 150161, doi:10.1016/j.coldregions.2007.12.003.

    • Search Google Scholar
    • Export Citation
  • Elíasson, A. J., E. Thorsteins, H. Ágústsson, and Ó. Rögnvaldsson, 2011: Comparison between simulations and measurements of in-cloud icing in test spans. Proc. 14th Int. Workshop on Atmospheric Icing of Structures (IWAIS), Chongqing, China, IWAIS, 7 pp. [Available online at http://iwais.compusult.net/html/IWAIS_Proceedings/IWAIS_2011/Papers/A4_5_231.pdf.]

  • Elíasson, A. J., H. Ágústsson, G. M. Hannesson, and E. Thorsteins, 2013: Modeling wet-snow accretion: Comparison of cylindrical model to field measurements. Proc. 15th Int. Workshop on Atmospheric Icing of Structures (IWAIS), St. John’s, NL, Canada, IWAIS, 9 pp. [Available online at http://www.landsnet.is/library/Skrar/Landsnet/Upplysingatorg/Frettir/2013/16.09.2013/IWAIS%20-%20Modeling%20wet-snow%20accretion_2013-09-02.pdf.]

  • Fikke, S., and Coauthors, 2007: COST 727: Atmospheric icing on structures, measurements and data collection on icing: State of the art. MeteoSwiss, 110 pp.

  • Finstad, K. J., E. P. Lozowski, and E. M. Gates, 1988: A computational investigation of water droplet trajectories. J. Atmos. Oceanic Technol., 5, 160170, doi:10.1175/1520-0426(1988)005<0160:ACIOWD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fouquart, Y., and B. Bonnel, 1980: Computation of solar heating of the Earth’s atmosphere: A new parameterization. Beitr. Phys. Atmos., 53, 3562.

    • Search Google Scholar
    • Export Citation
  • Garand, L., 1983: Some improvements and complements to the infrared emissivity algorithm including a parameterization of the absorption in the continuum region. J. Atmos. Sci., 40, 230244, doi:10.1175/1520-0469(1983)040<0230:SIACTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Heimo, A., R. Cattin, and B. Calpini, 2009: Recommendations for meteorological measurements under icing conditions. Proc. 13th Int. Workshop on Atmospheric Icing of Structures (IWAIS), Andermatt, Switzerland, IWAIS, 5 pp. [Available online at http://iwais.compusult.net/html/IWAIS_Proceedings/IWAIS_2009/Session_4_cost_727_wg2/session_4_heimo.pdf.]

  • Hošek, J., 2007: Icing measurements at Milešovka and their comparison with reanalysis and mesoscale model outputs. Proc. 12th Int. Workshop on Atmospheric Icing of Structures (IWAIS), Yokohama, Japan, IWAIS, 5 pp. [Available online at http://iwais.compusult.net/html/IWAIS_Proceedings/IWAIS_2007/Category3%20Field%20Observations,%20Data%20Gathering%20and%20Instrumentation/Lecture/Hosek.pdf.]

  • Houston, T. G., and S. A. Changnon, 2007: Freezing rain events: A major weather hazard in the conterminous U.S. Nat. Hazards, 40, 485494, doi:10.1007/s11069-006-9006-0.

    • Search Google Scholar
    • Export Citation
  • International Organization for Standardization, 2001: Atmospheric icing of structures. ISO 12494, Geneva, Switzerland, 56 pp.

  • Jones, K. F., 1996: Ice accretion in freezing rain. CRREL Rep. 96-2, 23 pp. [Available from Cold Regions Research and Engineering Laboratory, 72 Lyme Rd., Hanover, NH 03755-1290.]

  • Jones, K. F., 1998: A simple model for freezing rain ice loads. Atmos. Res., 46, 8797, doi:10.1016/S0169-8095(97)00053-7.

  • Karlsson, T., V. Turkia, and T. Wallenius, 2013: Icing production loss module for wind power forecasting system. VTT, 20 pp. [Available online at http://www.vtt.fi/inf/pdf/technology/2013/T139.pdf.]

  • Lacroix, A., and M. Tan, 2012: Assessment of wind energy production penalties due to cold climate in Canada. Natural Resources Canada Tech. Rep., 35 pp. [Catalog No. M154-68/2012E-PDF; ISBN 978-1-100-21299-9.]

  • Mailhot, J., and R. Benoit, 1982: A finite-element model of the atmospheric boundary-layer suitable for use with numerical weather prediction models. J. Atmos. Sci., 39, 22492266, doi:10.1175/1520-0469(1982)039<2249:AFEMOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mailhot, J., and C. Chouinard, 1989: Numerical forecasts of explosive winter storms: Sensitivity experiments with a meso-α scale model. Mon. Wea. Rev., 117, 13111343, doi:10.1175/1520-0493(1989)117<1311:NFOEWS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Makkonen, L., 1984: Modeling of ice accretion on wires. J. Climate Appl. Meteor., 23, 929939, doi:10.1175/1520-0450(1984)023<0929:MOIAOW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Makkonen, L., 1996: Modeling power line icing in freezing precipitation. Proc. Seventh Int. Workshop on Atmospheric Icing of Structures (IWAIS), Chicoutimi, QC, Canada, IWAIS, 195201.

  • Makkonen, L., 1998: Modeling power line icing in freezing precipitation. Atmos. Res., 46, 131142, doi:10.1016/S0169-8095(97)00056-2.

  • Makkonen, L., 2000: Models for the growth of rime, glaze, icicles and wet snow on structures. Philos. Trans. Roy. Soc. London, 358A, 29132939, doi:10.1098/rsta.2000.0690.

    • Search Google Scholar
    • Export Citation
  • Makkonen, L., and B. Wichura, 2010: Simulating wet snow loads on power line cables by a simple model. Cold Reg. Sci. Technol., 61, 7381, doi:10.1016/j.coldregions.2010.01.008.

    • Search Google Scholar
    • Export Citation
  • Milbrandt, J., and M. K. Yau, 2005: A multimoment bulk microphysics parameterization. Part II: A proposed three-moment closure and scheme description. J. Atmos. Sci., 62, 30653081, doi:10.1175/JAS3535.1.

    • Search Google Scholar
    • Export Citation
  • Milbrandt, J., M. K. Yau, J. Mailhot, S. Belair, and R. McTaggart-Cowan, 2010: Simulation of an orographic precipitation event during IMPROVE-2. Part II: Sensitivity to the number of moments in the bulk microphysics scheme. Mon. Wea. Rev., 138, 625642, doi:10.1175/2009MWR3121.1.

    • Search Google Scholar
    • Export Citation
  • Morgan, C., and E. Bossanyi, 1996: Wind turbine icing and public safety—A quantifiable risk? Proc. BOREAS III: Wind Energy Production in Cold Climates, Saariselkä, Finland, Finnish Meteorological Institute.

  • Morgan, C., E. Bossanyi, and H. Seifert, 1998: Assessment of safety risks arising from wind turbine icing. Proc. BOREAS IV: Wind Energy Production in Cold Climates, Hetta, Finland, Finnish Meteorological Institute, 113–121. [Available online at http://virtual.vtt.fi/virtual/arcticwind/boreasiv/assessment_of_safety.pdf.]

  • Nygaard, B. E. K., 2009: Evaluation of icing simulations for the “COST 727 icing test sites” in Europe. Proc. 13th Int. Workshop on Atmospheric Icing of Structures (IWAIS), Andermatt, Switzerland, IWAIS, 5 pp. [Available online at http://iwais.compusult.net/html/IWAIS_Proceedings/IWAIS_2009/Session_3_cost_727_wg1/session_3_nygaard.pdf.]

  • Nygaard, B. E. K., S. M. Fikke, L. Elvertrø, and K. Harstveit, 2007a: Modeling icing in exposed mountain terrain. Proc. 12th Int. Workshop on Atmospheric Icing of Structures (IWAIS), Yokohama, Japan, IWAIS, 4 pp. [Available online at http://iwais.compusult.net/html/IWAIS_Proceedings/IWAIS_2007/Category2_Modeling_Simulation_of_Icing/Lecture/Nygaard_1.pdf.]

  • Nygaard, B. E. K., J. E. Kristjánsson, E. Berge, and L. Makkonen, 2007b: Using NWP models to simulate in-cloud atmospheric icing episodes. Proc. 12th Int. Workshop on Atmospheric Icing of Structures (IWAIS), Yokohama, Japan, IWAIS, 4 pp. [Available online at http://iwais.compusult.net/html/IWAIS_Proceedings/IWAIS_2007/Category2_Modeling_Simulation_of_Icing/Lecture/Nygaard_2.pdf.]

  • Nygaard, B. E. K., J. E. Kristjánsson, and L. Makkonen, 2011: Prediction of in-cloud icing conditions at ground level using the WRF Model. J. Appl. Meteor. Climatol., 50, 24452459, doi:10.1175/JAMC-D-11-054.1.

    • Search Google Scholar
    • Export Citation
  • Nygaard, B. E. K., B. Egil, H. Ágústsson, and K. Somfalvi-Tóth, 2013: Modeling wet snow accretion on power lines: Improvements to previous methods using 50 years of observations. J. Appl. Meteor. Climatol., 52, 21892203, doi:10.1175/JAMC-D-12-0332.1.

    • Search Google Scholar
    • Export Citation
  • Paquin-Ricard, D., C. Jones, and P. A. Vaillancourt, 2010: Using ARM observations to evaluate cloud and clear-sky radiation processes as simulated by the Canadian regional climate model GEM. Mon. Wea. Rev., 138, 818838, doi:10.1175/2009MWR2745.1.

    • Search Google Scholar
    • Export Citation
  • Podolskiy, E. A., B. E. K. Nygaard, K. Nishimura, L. Makkonen, and E. P. Lozowski, 2012: Study of unusual atmospheric icing at Mount Zao, Japan, using the Weather Research and Forecasting model. J. Geophys. Res., 117, D12106, doi:10.1029/2011JD017042.

    • Search Google Scholar
    • Export Citation
  • Rong, J. Q., and N. Bose, 1990: Power reduction from ice accretion on a horizontal axis wind turbine. Proc. 12th British Wind Energy Association Conf., Norwich, UK, British Wind Energy Association, 77–86.

  • Seifert, H., A. Westerhellweg, and J. Kröning, 2003: Risk analysis of ice throw from wind turbines. Proc. BOREAS VI: Wind Energy Production in Cold Climates., Pyhä, Finland, Finnish Meteorological Institute, 18 pp. [Available online at http://www.windaction.org/posts/13298-risk-analysis-of-ice-throw-from-wind-turbines#.VV1FNyg_tnE.]

  • Soderberg, S., and M. Baltscheffsky, 2012: Long-term estimates and variability of production losses in icing climates. Winterwind Int. Wind Energy Conf., Skellefteå, Sweden, Swedish Wind Power Association, 17 pp. [Available online at http://www.slideshare.net/WinterwindConference/longterm-estimates-and-variability-of-production-losses-in-icing-climates-stefan-sderberg-magnus-baltscheffsky-weathertech-scandinavia.]

  • Sørensen, J. D., J. N. Sørensen, and J. Lemming, 2012: Risk assessment of wind turbines close to highways. Proc. EWEA Annual Event, Copenhagen, Denmark, European Wind Energy Association, 9 pp. [Available online at http://proceedings.ewea.org/annual2012/allfiles2/969_EWEA2012presentation.pdf.]

  • Turkia, V., S. Huttunen, and T. Wallenius, 2013: Method for estimating wind turbulence production losses due to icing. VTT Technology 114, 44 pp. [Available online at http://www.vtt.fi/inf/pdf/technology/2013/T114.pdf.]

  • Wakahama, G., 1979: Experimental studies of snow accretion on electric lines developed in a strong wind. J. Nat. Disaster Sci., 1, 2133.

    • Search Google Scholar
    • Export Citation
  • Wareing, J. B., and B. E. K. Nygaard, 2009: WRF simulation of wet snow and rime icing incidents in the UK. Proc. 13th Int. Workshop on Atmospheric Icing of Structures (IWAIS), Andermatt, Switzerland, IWAIS, 3 pp. [Available online at http://iwais.compusult.net/html/IWAIS_Proceedings/IWAIS_2009/Session_3_cost_727_wg1/session_3_wareing.pdf.]

  • Yang, D., H. Ritchie, S. Desjardins, G. Pearson, A. MacAfee, and I. Gultepe, 2010: High-resolution GEM-LAM application in marine fog prediction: Evaluation and diagnosis. Wea. Forecasting, 25, 727748, doi:10.1175/2009WAF2222337.1.

    • Search Google Scholar
    • Export Citation
  • Yang, J., K. F. Jones, W. Yu, and R. Morris, 2012: Simulation of in-cloud icing events on Mount Washington with the GEM-LAM. J. Geophys. Res., 117, D17204, doi:10.1029/2012JD017520.

    • Search Google Scholar
    • Export Citation
  • Yu, W., L. Garand, and A. P. Dastoor, 1997: Evaluation of model clouds and radiation at 100 km scale using GOES data. Tellus, 49A, 246262, doi:10.1034/j.1600-0870.1997.t01-1-00006.x.

    • Search Google Scholar
    • Export Citation
  • Yu, W., and Coauthors, 2014: An operational application of NWP models in a wind power forecasting demonstration experiment. Wind Eng., 38, 121, doi:10.1260/0309-524X.38.1.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1216 834 65
PDF Downloads 411 111 15