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

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  • Casella, Livio

Abstract

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.

Suggested Citation

  • Casella, Livio, 2015. "Performance analysis of the first method for long-term turbulence intensity estimation at potential wind energy sites," Renewable Energy, Elsevier, vol. 74(C), pages 106-115.
    • Handle: RePEc:eee:renene:v:74:y:2015:i:c:p:106-115
    DOI: 10.1016/j.renene.2014.07.031
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    1. Carta, José A. & Velázquez, Sergio & Cabrera, Pedro, 2013. "A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 362-400.
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    1. Arenas-López, J. Pablo & Badaoui, Mohamed, 2020. "Stochastic modelling of wind speeds based on turbulence intensity," Renewable Energy, Elsevier, vol. 155(C), pages 10-22.
    2. Enevoldsen, Peter, 2016. "Onshore wind energy in Northern European forests: Reviewing the risks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1251-1262.
    3. Han, Qinkai & Hao, Zhuolin & Hu, Tao & Chu, Fulei, 2018. "Non-parametric models for joint probabilistic distributions of wind speed and direction data," Renewable Energy, Elsevier, vol. 126(C), pages 1032-1042.

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