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Modelling of turbulent wind flow using the embedded Markov chain method

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  • Evans, S.P.
  • Clausen, P.D.

Abstract

Small wind turbines are usually installed to provide off-grid power and as such can be situated close to the load in a less-than-ideal wind resource. These wind regimes are often governed by low mean speeds and high wind turbulence. This can result in energy production less than that specified by the manufacturer's power curve. Wind turbulence is detrimental to the fatigue life of key components and overall turbine reliability and therefore must be considered in the design stage of small wind turbines. Consequently it is important to accurately simulate wind speed data at highly turbulent sites to quantify loading on turbine components. Here we simulate wind speed data using the Markov chain Monte Carlo process and incorporate long term effects using an embedded Markov chain. First, second and third order Markov chain predictions were found to be in good agreement with measured wind data acquired at 1 Hz. The embedded Markov chain was able to predict site turbulent intensity with a reasonable degree of accuracy. The site exhibited distinctive peaks in wind speed possibly caused by diurnal heating and cooling of the earth's surface. The embedded Markov chain method was able to simulate these peaks albeit with a time offset.

Suggested Citation

  • Evans, S.P. & Clausen, P.D., 2015. "Modelling of turbulent wind flow using the embedded Markov chain method," Renewable Energy, Elsevier, vol. 81(C), pages 671-678.
    • Handle: RePEc:eee:renene:v:81:y:2015:i:c:p:671-678
    DOI: 10.1016/j.renene.2015.03.067
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    References listed on IDEAS

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    6. Ghaemi, Zahra & Tran, Thomas T.D. & Smith, Amanda D., 2022. "Comparing classical and metaheuristic methods to optimize multi-objective operation planning of district energy systems considering uncertainties," Applied Energy, Elsevier, vol. 321(C).

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