Performance analysis of the first method for long-term turbulence intensity estimation at potential wind energy sites

The paper presents a validation test of a recent algorithm implemented by the author to correlate turbulence intensity (TI) data recorded at two meteorological masts and based on the conditional probability to measure simultaneous events of wind speed, direction and TI. Two testing sites, located about 5 km apart from each other in a hilly terrain, in the South of Australia, are considered in this work. Three years of concurrent data (2005–2008) are analyzed to estimate a long-term (LT) representative TI. A complete examination of the scores is carried out by spanning dimension and temporal period of the data samples used in the correlation analysis. Root mean square error, committed by the method to approximate mean value of TI measured in each of the three years, can be correlated with number of used months by exponential decay functions. The intermonthly variations stronger affect the accuracy of the results than the yearly ones. However, the average errors are always moderate and good performances are achieved for all the considered wind speed thresholds and also when examining different periods of the year. The tested methodology represents an important step through standardization of Measure-correlate-predict (MCP) technique for TI assessment.

[1]  Erik Lundtang Petersen,et al.  The European Wind Atlas , 1985 .

[2]  Livio Casella Improving Long-Term Wind Speed Assessment using Joint Probability Functions Applied to Three Wind Data Sets , 2012 .

[3]  B. Efron The jackknife, the bootstrap, and other resampling plans , 1987 .

[4]  N. Mortensen,et al.  WAsP prediction errors due to site orography , 2004 .

[5]  W. Briggs Statistical Methods in the Atmospheric Sciences , 2007 .

[6]  J. Holton An introduction to dynamic meteorology , 2004 .

[7]  Ramón García-Rojo Algorithm for the Estimation of the Long-Term Wind Climate at a Meteorological Mast Using a Joint Probabilistic Approach , 2004 .

[8]  Andreas Bechmann,et al.  Hybrid RANS/LES method for wind flow over complex terrain , 2010 .

[9]  J. A. Carta,et al.  A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site , 2013 .

[10]  Livio Casella A Measure-Correlation-Predict Method for Turbulence Intensity Distribution Assessment , 2013 .

[11]  James F. Manwell,et al.  Uncertainties in Results of Measure-Correlate-Predict Analyses , 2006 .

[12]  Livio Casella,et al.  Dynamic flow analysis using an OpenFOAM based CFD tool: Validation of Turbulence Intensity in a testing site , 2014 .

[13]  S. Frandsen Turbulence and turbulence-generated structural loading in wind turbine clusters , 2007 .

[14]  Joel H. Ferziger,et al.  Computational methods for fluid dynamics , 1996 .

[15]  Ravita D. Prasad,et al.  Technologies and Methods used in Wind Resource Assessment , 2011 .

[16]  J. A. Carta,et al.  A joint probability density function of wind speed and direction for wind energy analysis , 2008 .

[17]  Niels Gylling Mortensen,et al.  Response of neutral boundary layers to changes of roughness , 1990 .

[18]  J. A. Carta,et al.  A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind , 2011 .