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Scheduling and conducting power performance testing of a small wind turbine

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  • Whale, J.
  • McHenry, M.P.
  • Malla, A.

Abstract

The global growth in small wind turbine (SWT) markets and in the number of SWT manufacturers has brought about an urgent need for more rigorous testing of SWTs in order to ensure safety, reliability and performance. This work presents modelling of the wind resource at the Australian National Small Wind Turbine Centre (NSWTC) test site to give insight into the scope and scheduling of power performance tests, and assess testing completion requirements for national and international SWT performance standards. Wind modelling of the long-term wind resource at the site was used to guide the NSWTC testing program and develop a tool to provide recommendations regarding suitable months for testing particular turbines. The predictions from the tool are compared to the results of testing a SOMA 1000 small wind turbine. The results indicate that current testing standards need to specify more than 10 min worth of data in each bin in order to reduce uncertainty errors in power curves, particularly at higher wind speeds and during furling and unfurling of the turbine. Furthermore, this work supports observations that there are often notable discrepancies between published SWT manufacturer power curves and test results at high wind speeds.

Suggested Citation

  • Whale, J. & McHenry, M.P. & Malla, A., 2013. "Scheduling and conducting power performance testing of a small wind turbine," Renewable Energy, Elsevier, vol. 55(C), pages 55-61.
    • Handle: RePEc:eee:renene:v:55:y:2013:i:c:p:55-61
    DOI: 10.1016/j.renene.2012.11.032
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    References listed on IDEAS

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    1. Bowen, A.J & Zakay, N & Ives, R.L, 2003. "The field performance of a remote 10 kW wind turbine," Renewable Energy, Elsevier, vol. 28(1), pages 13-33.
    2. Ross, S.J. & McHenry, M.P. & Whale, J., 2012. "The impact of state feed-in tariffs and federal tradable quota support policies on grid-connected small wind turbine installed capacity in Australia," Renewable Energy, Elsevier, vol. 46(C), pages 141-147.
    3. Kamp, Linda M. & Smits, Ruud E. H. M. & Andriesse, Cornelis D., 2004. "Notions on learning applied to wind turbine development in the Netherlands and Denmark," Energy Policy, Elsevier, vol. 32(14), pages 1625-1637, September.
    4. Whale, Jonathan, 2009. "Design and construction of a simple blade pitch measurement system for small wind turbines," Renewable Energy, Elsevier, vol. 34(2), pages 425-429.
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    Cited by:

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    2. Marino Marrocu & Luca Massidda, 2017. "A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast," Energies, MDPI, vol. 10(12), pages 1-14, November.
    3. Tabrizi, Amir Bashirzadeh & Whale, Jonathan & Lyons, Thomas & Urmee, Tania, 2015. "Rooftop wind monitoring campaigns for small wind turbine applications: Effect of sampling rate and averaging period," Renewable Energy, Elsevier, vol. 77(C), pages 320-330.
    4. Emejeamara, F.C. & Tomlin, A.S., 2020. "A method for estimating the potential power available to building mounted wind turbines within turbulent urban air flows," Renewable Energy, Elsevier, vol. 153(C), pages 787-800.
    5. Lydia, M. & Kumar, S. Suresh & Selvakumar, A. Immanuel & Prem Kumar, G. Edwin, 2014. "A comprehensive review on wind turbine power curve modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 452-460.
    6. 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.

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