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An Efficient Estimation of Wind Turbine Output Power Using Neural Networks

Author

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  • Muhammad Yaqoob Javed

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan)

  • Iqbal Ahmed Khurshid

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan)

  • Aamer Bilal Asghar

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan)

  • Syed Tahir Hussain Rizvi

    (Dipartimento di Elettronica e Telecomunicazioni (DET), Politecnico di Torino, 10129 Torino, Italy)

  • Kamal Shahid

    (Department of Electronic Systems, Aalborg University, 9220 Aalborg, Denmark)

  • Krzysztof Ejsmont

    (Faculty of Mechanical and Industrial Engineering, Warsaw University of Technology, 02-524 Warsaw, Poland)

Abstract

Wind energy is a valuable source of electric power as its motion can be converted into mechanical energy, and ultimately electricity. The significant variability of wind speed calls for highly robust estimation methods. In this study, the mechanical power of wind turbines (WTs) is successfully estimated using input variables such as wind speed, angular speed of WT rotor, blade pitch, and power coefficient (Cp). The feed-forward backpropagation neural networks (FFBPNNs) and recurrent neural networks (RNNs) are incorporated to perform the estimations of wind turbine output power. The estimations are performed based on diverse parameters including the number of hidden layers, learning rates, and activation functions. The networks are trained using a scaled conjugate gradient (SCG) algorithm and evaluated in terms of the root mean square error (RMSE) and mean absolute percentage error (MAPE) indices. FFBPNN shows better results in terms of RMSE (0.49%) and MAPE (1.33%) using two and three hidden layers, respectively. The study indicates the significance of optimal selection of input parameters and effects of changing several hidden layers, activation functions, and learning rates to achieve the best performance of FFBPNN and RNN.

Suggested Citation

  • Muhammad Yaqoob Javed & Iqbal Ahmed Khurshid & Aamer Bilal Asghar & Syed Tahir Hussain Rizvi & Kamal Shahid & Krzysztof Ejsmont, 2022. "An Efficient Estimation of Wind Turbine Output Power Using Neural Networks," Energies, MDPI, vol. 15(14), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5210-:d:865725
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    References listed on IDEAS

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    1. Yongsheng Zhao & Jianmin Yang & Yanping He, 2012. "Preliminary Design of a Multi-Column TLP Foundation for a 5-MW Offshore Wind Turbine," Energies, MDPI, vol. 5(10), pages 1-18, October.
    2. Marčiukaitis, Mantas & Žutautaitė, Inga & Martišauskas, Linas & Jokšas, Benas & Gecevičius, Giedrius & Sfetsos, Athanasios, 2017. "Non-linear regression model for wind turbine power curve," Renewable Energy, Elsevier, vol. 113(C), pages 732-741.
    3. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    4. Chang, G.W. & Lu, H.J. & Chang, Y.R. & Lee, Y.D., 2017. "An improved neural network-based approach for short-term wind speed and power forecast," Renewable Energy, Elsevier, vol. 105(C), pages 301-311.
    5. Lago, Lucas I. & Ponta, Fernando L. & Otero, Alejandro D., 2013. "Analysis of alternative adaptive geometrical configurations for the NREL-5 MW wind turbine blade," Renewable Energy, Elsevier, vol. 59(C), pages 13-22.
    6. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
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    Cited by:

    1. Muhammad Umair Safder & Mohammad J. Sanjari & Ameer Hamza & Rasoul Garmabdari & Md. Alamgir Hossain & Junwei Lu, 2023. "Enhancing Microgrid Stability and Energy Management: Techniques, Challenges, and Future Directions," Energies, MDPI, vol. 16(18), pages 1-28, September.

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