Non-linear regression model for wind turbine power curve

In this article, a study of wind turbine power curve modelling is presented with application to a particular wind turbine of Seirijai wind farm in Lithuania. A non-linear regression model for wind turbine power curve approximation was proposed, which stands out with several advantages, such as fitting physical properties of wind turbine (i.e., power curve does not exceed the highest value of generated power as it is maximum physically possible), lower number of parameters to be estimated, dependency on only one factor. MAPE was used as a measure of approximation method accuracy. Mode approach was introduced as an alternative to typical techniques for modelling power curves of wind turbines with the aim to avoid elimination of the outliers from initial data and the impact of varying concentration of observations in the full range of wind speed. Performed cross-validation analysis demonstrated that the developed power curve model is appropriate for the prediction of wind power and is not directly dependent on the initial data set.

[1]  Seref Sagiroglu,et al.  A new approach to very short term wind speed prediction using k-nearest neighbor classification , 2013 .

[2]  Paul Gipe,et al.  Wind Power: Renewable Energy for Home, Farm and Business , 2004 .

[3]  Carsten Croonenbroeck,et al.  A selection of time series models for short- to medium-term wind power forecasting , 2015 .

[4]  Stefano Alessandrini,et al.  A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data , 2013 .

[5]  Rik Van de Walle,et al.  Data-driven multivariate power curve modeling of offshore wind turbines , 2016, Eng. Appl. Artif. Intell..

[6]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[7]  Antonio Messineo,et al.  Monitoring of wind farms’ power curves using machine learning techniques , 2012 .

[8]  J. Cidrás,et al.  Review of power curve modelling for wind turbines , 2013 .

[9]  V. K. Sethi,et al.  Critical analysis of methods for mathematical modelling of wind turbines , 2011 .

[10]  Loı̈c Quéval,et al.  Measuring the Power Curve of a Small-Scale Wind Turbine: A Practical Example , 2014 .

[11]  H. J. Lu,et al.  An improved neural network-based approach for short-term wind speed and power forecast , 2017 .

[12]  Andrés Feijóo,et al.  Reformulation of parameters of the logistic function applied to power curves of wind turbines , 2016 .

[13]  George Galanis,et al.  Wind power prediction based on numerical and statistical models , 2013 .

[14]  Madeleine Gibescu,et al.  A dynamic wind farm aggregate model for the simulation of power fluctuations due to wind turbulence , 2010, J. Comput. Sci..

[15]  Antoine Tahan,et al.  Wind turbine power curve modelling using artificial neural network , 2016 .

[16]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[17]  Christian M. Dahl,et al.  Accurate medium-term wind power forecasting in a censored classification framework , 2014 .

[18]  Xsitaaz T. Chadee,et al.  Large-scale wind energy potential of the Caribbean region using near-surface reanalysis data , 2014 .

[19]  J. Cidras,et al.  An Approach to Determine the Weibull Parameters for Wind Energy Analysis: The Case of Galicia (Spain) , 2014 .

[20]  Benoit Dalpé,et al.  Numerical simulation of wind flow near a forest edge , 2009 .

[21]  Luisa Pagnini,et al.  Experimental power curve of small-size wind turbines in turbulent urban environment , 2015 .

[22]  Mostafa Modiri-Delshad,et al.  Development of an enhanced parametric model for wind turbine power curve , 2016 .

[23]  A. Immanuel Selvakumar,et al.  A comprehensive review on wind turbine power curve modeling techniques , 2014 .