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Wind Data Analysis, Annual Resource Estimation And Comparison With Measured Annual Energy Yield at the University Wind Turbine

Author

Listed:
  • James Maina Muriithi
  • Harrison Ngetha
  • Jean Byiringiro
  • Kevin Volkmer
  • Thomas Carolus

Abstract

Evaluation of the power potential of a particular type of wind turbine at a specific site is necessary for economic decisions. Therefore, the information of a wind turbine and that of a site have to be measured or predicted and then combined with the power curve of a wind turbine. The main objective of this research was to predict the power potential of the existing small wind turbine with a diameter of 3m and the wind turbine site at the University of Siegen and compare with the annual energy calculated from the measured one year of wind and turbine data. Techniques for prediction of the wind speed distribution of a site were determined and modeled. The power curve of the wind turbine was modeled from data recorded by applying a technique from the novel methods for modelling the power curve. In this research, artificial neural network, Weibull and Rayleigh are the techniques modeled to predict wind speed distribution at the wind turbine site. Rayleigh and Weibull were chosen since the two models depict a better wind speed distribution and require the mean and the standard deviation of the wind speed at the wind turbine site. A neural network trained with the backward propagation levernberg-Marquardt algorithm was applied to predict the wind speed and power potential of the wind turbine site. A comparison between Weibull, Rayleigh and the Levernberg-Marquardt trained neural network wind speed was made. The power curve of the wind turbine was successfully evaluated from wind data and wind turbine data recorded. The results indicate that the annual mean wind speed of the region is 2.54 (m/s) and about 20% of the wind availability was blowing from the west. The annual energy yield predicted from the trained neural network was 372 (kWh) closer to that determined from measured wind speed 360 (kWh) than that determined from Weibull and Rayleigh 337 and 233 (kWh) respectively. The three prediction models are applicable in any region to predict the annual energy of a particular wind turbine site with minimal data available.

Suggested Citation

  • James Maina Muriithi & Harrison Ngetha & Jean Byiringiro & Kevin Volkmer & Thomas Carolus, 2019. "Wind Data Analysis, Annual Resource Estimation And Comparison With Measured Annual Energy Yield at the University Wind Turbine," European Journal of Engineering and Technology Research, European Open Science, vol. 4(6), pages 25-33, June.
    • Handle: RePEc:epw:ejeng0:v:4:y:2019:i:6:id:61355
    DOI: 10.24018/ejeng.2019.4.6.1355
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    1. Mathew, Sathyajith & Pandey, K.P. & Kumar.V, Anil, 2002. "Analysis of wind regimes for energy estimation," Renewable Energy, Elsevier, vol. 25(3), pages 381-399.
    2. Gnatowska, Renata & Moryń-Kucharczyk, Elżbieta, 2019. "Current status of wind energy policy in Poland," Renewable Energy, Elsevier, vol. 135(C), pages 232-237.
    3. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
    4. Ashok, S., 2007. "Optimised model for community-based hybrid energy system," Renewable Energy, Elsevier, vol. 32(7), pages 1155-1164.
    5. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    6. Safari, Bonfils & Gasore, Jimmy, 2010. "A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda," Renewable Energy, Elsevier, vol. 35(12), pages 2874-2880.
    7. Birgir Hrafnkelsson & Gudmundur V. Oddsson & Runar Unnthorsson, 2016. "A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation," Energies, MDPI, vol. 9(4), pages 1-14, April.
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