Identifying and characterizing the impact of turbine icing on wind farm power generation: Impact of turbine icing on wind farm production

Wind park power production in cold climate regions is significantly impacted by ice growth on turbine blades. This can lead to significant errors in power forecasts and in the estimation of expected power production during turbine siting. A modeling system is presented that uses a statistical modeling approach to estimate the power loss due to icing, using inputs from both a physical icing and a numerical weather prediction model. The physical icing model is that of Davis et al., [1]with updates to the simulation of ice ablation. A new approach for identifying periods of turbine blade icing from power observations was developed and used to calculate the observed power loss caused by icing. The observed icing power loss for 2years at six wind parks was used to validate the modeling system performance. Production estimates using the final production loss model reduce the root mean squared error when compared with the empirical wind park power curve (without icing influence) at five of the six wind parks while reducing the mean bias at all six wind parks. In addition to performing well when fit to each wind park, the production loss model was shown to improve the estimate of power when fit using all six wind parks, suggesting it may also be useful for wind parks where production data are not available. Copyright © 2015 John Wiley & Sons, Ltd.

[1]  M. Kendall A NEW MEASURE OF RANK CORRELATION , 1938 .

[2]  A. Sorteberg,et al.  Dynamical downscaling of ERA-40 in complex terrain using the WRF regional climate model , 2011 .

[3]  Lasse Makkonen,et al.  Models for the growth of rime, glaze, icicles and wet snow on structures , 2000, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[4]  John S. Kain,et al.  The Kain–Fritsch Convective Parameterization: An Update , 2004 .

[5]  N. P. Woodruff,et al.  The Physics of Wind Erosion and its Control1 , 1963 .

[6]  Z. Janjic The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes , 1994 .

[7]  J. Dudhia,et al.  A Revised Approach to Ice Microphysical Processes for the Bulk Parameterization of Clouds and Precipitation , 2004 .

[8]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part II: Preliminary Model Validation , 2001 .

[9]  Per Johan Nicklasson,et al.  Ice sensors for wind turbines , 2006 .

[10]  W. J. Steenburgh,et al.  Evaluation of Surface Sensible Weather Forecasts by the WRF and the Eta Models over the Western United States , 2005 .

[11]  N. Bridges,et al.  Ventifacts on Earth and Mars: Analytical, field, and laboratory studies supporting sand abrasion and windward feature development , 2009 .

[12]  E. Mlawer,et al.  Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave , 1997 .

[13]  Gregor Giebel,et al.  The State-Of-The-Art in Short-Term Prediction of Wind Power. A Literature Overview , 2003 .

[14]  J. Dudhia Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model , 1989 .

[15]  Neil Davis,et al.  Forecast of Icing Events at a Wind Farm in Sweden , 2014 .

[16]  R. Dennis Cook,et al.  Cross-Validation of Regression Models , 1984 .

[17]  S. Wood Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models , 2011 .

[18]  S. Wood Thin plate regression splines , 2003 .

[19]  J. Jonkman,et al.  Definition of a 5-MW Reference Wind Turbine for Offshore System Development , 2009 .