Advertisement

Comparison of modeling methods for wind power prediction: a critical study

  • Rashmi P. Shetty
  • A. Sathyabhama
  • P. Srinivasa Pai
Research Article
  • 46 Downloads

Abstract

Prediction of power generation of a wind turbine is crucial, which calls for accurate and reliable models. In this work, six different models have been developed based on wind power equation, concept of power curve, response surface methodology (RSM) and artificial neural network (ANN), and the results have been compared. To develop the models based on the concept of power curve, the manufacturer’s power curve, and to develop RSM as well as ANN models, the data collected from supervisory control and data acquisition (SCADA) of a 1.5 MW turbine have been used. In addition to wind speed, the air density, blade pitch angle, rotor speed and wind direction have been considered as input variables for RSM and ANN models. Proper selection of input variables and capability of ANN to map input-output relationships have resulted in an accurate model for wind power prediction in comparison to other methods.

Keywords

power curve method of least squares cubic spline interpolation response surface methodology artificial neural network (ANN) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    International Energy Agency (IEA). Technology Roadmap: Wind Energy. 2013, http://www.iea.org/publications/freepublications/publication/name,43771,en.htmlGoogle Scholar
  2. 2.
    Sangroya D, Jogendra K N. Development of wind energy in India. International Journal of Renewable Energy Research, 2015, 5(1): 1–13Google Scholar
  3. 3.
    International Energy Agency (IEA). World energy outlook—2013. 2013, https://www.iea.org/publications/freepublications/publication/WEO2013.pdfGoogle Scholar
  4. 4.
    Razavieh A, Sedaghat A, Ayodele R, Mostafaeipour A. Worldwide wind energy status and the characteristics of wind energy in Iran, case study: the province of Sistan and Baluchestan. International Journal of Sustainable Energy, 2017, 36(2): 103–123CrossRefGoogle Scholar
  5. 5.
    Ipakchi A, Albuyeh F. Grid of the future. IEEE Power & Energy Magazine, 2009, 7(2): 52–62CrossRefGoogle Scholar
  6. 6.
    Han S, Yang Y, Liu Y. The comparison of BP network and RBF network in wind power prediction application. In: Proceedings of Second International Conference on Bio-Inspired Computing: Theories and Applications. 2007, 173–176Google Scholar
  7. 7.
    Ayodele T R, Ogunjuyigbe A S. Wind energy resource, wind energy conversion system modelling and integration: a survey. International Journal of Sustainable Energy, 2015, 34(10): 657–671CrossRefGoogle Scholar
  8. 8.
    Fang D, Wang J. A novel application of artificial neural network for wind speed estimation. International Journal of Sustainable Energy, 2017, 36(5): 415–429CrossRefGoogle Scholar
  9. 9.
    Wang Z, Wang W, Wang B. Regional wind power forecasting model with NWP grid data optimized. Frontiers in Energy, 2017, 11(2): 175–183CrossRefGoogle Scholar
  10. 10.
    Kaur S, Verma Y P, Agrawal S. Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment. Frontiers in Energy, 2013, 7(4): 468–478CrossRefGoogle Scholar
  11. 11.
    Rezvani A, Esmaeily A, Etaati H, Mohammadinodoushan M. Intelligent hybrid power generation system using new hybrid fuzzyneural for photovoltaic system and RBFNSM for wind turbine in the grid connected mode. Frontiers in Energy, 2017, https://doi.org/10.1007/s11708-017-0446-xGoogle Scholar
  12. 12.
    International Electrotechnical Commission.Wind turbine generator systems–Part 12: wind turbine power performance testing. 1998, IEC61400–12, https://webstore.iec.ch/p preview/info_iec61400–12%7Bed1.0%7Den.pdfGoogle Scholar
  13. 13.
    Thapar V, Agnihotri G, Sethi V K. Critical analysis of methods for mathematical modelling of wind turbines. Renewable Energy, 2011, 36(11): 3166–3177CrossRefGoogle Scholar
  14. 14.
    Shokrzadeh S, Jafari Jozani M, Bibeau E. Wind turbine power curve modeling using advanced parametric and nonparametric methods. IEEE Transactions on Sustainable Energy, 2014, 5(4): 1262–1269CrossRefGoogle Scholar
  15. 15.
    Lydia M, Kumar S S, Selvakumar A I, Prem Kumar G E. A comprehensive review on wind turbine power curve modeling techniques. Renewable & Sustainable Energy Reviews, 2014, 30: 452–460CrossRefGoogle Scholar
  16. 16.
    Marvuglia A, Messineo A. Monitoring of wind farms’ power curves using machine learning techniques. Applied Energy, 2012, 98: 574–583CrossRefGoogle Scholar
  17. 17.
    Üstüntas T, Sahin A D. Wind turbine power curve estimation based on cluster center fuzzy logic modeling. Journal ofWind Engineering and Industrial Aerodynamics, 2008, 96(5): 611–620CrossRefGoogle Scholar
  18. 18.
    Kusiak A, Zheng H, Song Z. Models for monitoring wind farm power. Renewable Energy, 2009, 34(3): 583–590CrossRefGoogle Scholar
  19. 19.
    Lydia M, Selvakumar A I, Kumar S S, Kumar G E. Advanced algorithms for wind turbine power curve modeling. IEEE Transactions on Sustainable Energy, 2013, 4(3): 827–835CrossRefGoogle Scholar
  20. 20.
    Carrillo C, Obando Montaño A F, Cidrás J, Díaz-Dorado E. Review of power curve modelling for wind turbines. Renewable & Sustainable Energy Reviews, 2013, 21: 572–581CrossRefGoogle Scholar
  21. 21.
    Gill S, Stephen B, Galloway S. Wind turbine condition assessment through power curve copula modeling. IEEE Transactions on Sustainable Energy, 2012, 3(1): 94–101CrossRefGoogle Scholar
  22. 22.
    Ouyang T, Kusiak A, He Y. Modeling wind-turbine power curve: a data partitioning and mining approach. Renewable Energy, 2017, 102: 1–8CrossRefGoogle Scholar
  23. 23.
    Goudarzi A, Davidson I E, Ahmadi A, Venayagamoorthy G K. Intelligent analysis of wind turbine power curve models. In: 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG), 2014, 1–7Google Scholar
  24. 24.
    Tu Y L, Chang T J, Chen C L, Chang Y J. Estimation of monthly wind power outputs of WECS with limited record period using artificial neural networks. Energy Conversion and Management, 2012, 59: 114–121CrossRefGoogle Scholar
  25. 25.
    Li S, Wunsch D C, O’Hair E, Giesselmann M G. Comparative analysis of regression and artificial neural network models for wind turbine power curve estimation. Journal of Solar Energy Engineering, 2001, 123(4): 327–332CrossRefGoogle Scholar
  26. 26.
    Liu Z, Gao W,Wan Y H, Muljadi E. Wind power plant prediction by using neural networks. In: 2012 IEEE Energy Conversion Congress and Exposition (ECCE), 2012, 3154–3160CrossRefGoogle Scholar
  27. 27.
    Schlechtingen M, Santos I F, Achiche S. Using data-mining approaches for wind turbine power curve monitoring: a comparative study. IEEE Transactions on Sustainable Energy, 2013, 4(3): 671–679CrossRefGoogle Scholar
  28. 28.
    Lapira E, Brisset D, Davari Ardakani H, Siegel D, Lee J. Wind turbine performance assessment using multi-regime modeling approach. Renewable Energy, 2012, 45: 86–95CrossRefGoogle Scholar
  29. 29.
    Mabel M C, Fernandez E. Analysis of wind power generation and prediction using ANN: a case study. Renewable Energy, 2008, 33 (5): 986–992CrossRefGoogle Scholar
  30. 30.
    Mabel M C, Fernandez E. Estimation of energy yield from wind farms using artificial neural networks. IEEE Transactions on Energy Conversion, 2009, 24(2): 459–464CrossRefGoogle Scholar
  31. 31.
    Reddy S S, Jung C M, Seog K J. Day-ahead electricity price forecasting using back propagation neural networks and weighted least square technique. Frontiers in Energy, 2016, 10(1): 105–113CrossRefGoogle Scholar
  32. 32.
    Kasiri H, Abadeh M S, Momeni H R. Optimal estimation and control of WECS via a genetic neuro fuzzy approach. Energy, 2012, 40(1): 438–444CrossRefGoogle Scholar
  33. 33.
    Kusiak A, Li W. Short-term prediction of wind power with a clustering approach. Renewable Energy, 2010, 35(10): 2362–2369CrossRefGoogle Scholar
  34. 34.
    Habib M A, Said S A, El-Hadidy M A, Al-Zaharna I. Optimization procedure of a hybrid photovoltaic wind energy system. Energy, 1999, 24(11): 919–929CrossRefGoogle Scholar
  35. 35.
    Abouzahr I, Ramakumar R. Loss of power supply probability of stand-alone wind electric conversion systems: a closed form solution approach. IEEE Transactions on Energy Conversion, 1990, 5(3): 445–452CrossRefGoogle Scholar
  36. 36.
    Abouzahr I, Ramakumar R. An approach to assess the performance of utility-interactive wind electric conversion systems. IEEE Transactions on Energy Conversion, 1991, 6(4): 627–638CrossRefGoogle Scholar
  37. 37.
    Yang H X, Lu L, Burnett J. Weather data and probability analysis of hybrid photovoltaic–wind power generation systems in Hong Kong. Renewable Energy, 2003, 28(11): 1813–1824CrossRefGoogle Scholar
  38. 38.
    Yang H, Lu L, Zhou W. A novel optimization sizing model for hybrid solar-wind power generation system. Solar Energy, 2007, 81 (1): 76–84CrossRefGoogle Scholar
  39. 39.
    Yang H, Wei Z, Chengzhi L. Optimal design and techno-economic analysis of a hybrid solar–wind power generation system. Applied Energy, 2009, 86(2): 163–169CrossRefGoogle Scholar
  40. 40.
    Ai B, Yang H, Shen H, Liao X. Computer-aided design of PV/wind hybrid system. Renewable Energy, 2003, 28(10): 1491–1512CrossRefGoogle Scholar
  41. 41.
    Diaf S, Diaf D, Belhamel M, Haddadi M, Louche A. A methodology for optimal sizing of autonomous hybrid PV/wind system. Energy Policy, 2007, 35(11): 5708–5718CrossRefGoogle Scholar
  42. 42.
    Hocaoglu F O, Gerek Ö N, Kurban M. A novel hybrid (wind–photovoltaic) system sizing procedure. Solar Energy, 2009, 83(11): 2019–2028CrossRefGoogle Scholar
  43. 43.
    Chandrasekaran S, Amarkarthik A, Sivakumar K, Selvamuthukumaran D, Sidney S. Experimental investigation and ANN modeling on improved performance of an innovative method of using heave response of a non-floating object for ocean wave energy conversion. Frontiers in Energy, 2013, 7(3): 279–287CrossRefGoogle Scholar
  44. 44.
    Kaur S, Verma Y P, Agrawal S. Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment. Frontiers in Energy, 2013, 7(4): 468–478CrossRefGoogle Scholar
  45. 45.
    Giwa S O, Adekomaya S O, Adama K O, Mukaila M O. Prediction of selected biodiesel fuel properties using artificial neural network. Frontiers in Energy, 2015, 9(4): 433–445CrossRefGoogle Scholar
  46. 46.
    Haykin S. Neural Networks: a Comprehensive Foundation. 2nd ed. New York: Pearson Education, 2009zbMATHGoogle Scholar
  47. 47.
    Chapra S C, Canale R C. Numerical Methods for Engineers. 6th ed. New York: McGraw-Hill 2010Google Scholar
  48. 48.
    Moghaddam M G, Khajeh M. Comparison of response surface methodology and artificial neural network in predicting the microwave-assisted extraction procedure to determine zinc in fish muscles. Food and Nutrition Sciences, 2011, 2(08): 803–808CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Rashmi P. Shetty
    • 1
  • A. Sathyabhama
    • 1
  • P. Srinivasa Pai
    • 2
  1. 1.Department of Mechanical EngineeringNational Institute of Technology KarnatakaSurathkalIndia
  2. 2.Department of Mechanical EngineeringNMAMITKarkalaTalukIndia

Personalised recommendations