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Efficient Modelling and Simulation Of Wind Power Using Online Sequential Learning Algorithm For Feed Forward Networks

Author

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  • Rashmi P. Shetty

    (Department of Mechanical engineering, National Institute of Technology Karnataka, 575025, India)

  • A. Sathyabhama

    (Department of Mechanical engineering, National Institute of Technology Karnataka, 575025, India)

  • Srinivasa Pai P.

    (Department of Mechanical engineering, NMAM Institute of Technology, Nitte, 574110, India)

Abstract

In this paper, an online sequential learning algorithm known as online sequential extreme learning machine (OS ELM) is applied to simulate the power output of a wind turbine. The OS ELM is used both in 1-by-1 and chunk-by-chunk mode and the results are compared with batch learning algorithms, namely Back Propagation (BP) and Extreme Learning Machine (ELM) algorithm. Different activation functions such as Sigmoidal, Sin, Radial Basis Function (RBF) and Hardlim have been used in OS ELM to decide upon most optimal function. It has been found that OS ELM with fixed chunk size of 50-by-50 and sigmoidal activation function with training time of 0.080s, Root Mean Square Error (RMSE) of 1.96%, prediction accuracies on training and test data of 100% and 99.95 % respectively, is best suited for wind power modelling and simulation applications, where the data arrives in a sequential manner.

Suggested Citation

  • Rashmi P. Shetty & A. Sathyabhama & Srinivasa Pai P., 2019. "Efficient Modelling and Simulation Of Wind Power Using Online Sequential Learning Algorithm For Feed Forward Networks," Journal of Mechanical Engineering Research & Developments (JMERD), Zibeline International Publishing, vol. 42(1), pages 109-115, March.
    • Handle: RePEc:zib:zjmerd:v:42:y:2019:i:1:p:109-115
    DOI: 10.26480/jmerd.01.2019.109.115
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    References listed on IDEAS

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    3. 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.
    4. Carolin Mabel, M. & Fernandez, E., 2008. "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, Elsevier, vol. 33(5), pages 986-992.
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