An Efficient Estimation of Wind Turbine Output Power Using Neural Networks

Muhammad Yaqoob Javed, Iqbal Ahmed Khurshid, Aamer Bilal Asghar, Syed Tahir Hussain Rizvi, Kamal Shahid and Krzysztof Ejsmont
Additional contact information
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

Energies, 2022, vol. 15, issue 14, 1-22

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.

Keywords: wind turbine; feed-forward back propagation neural network; recurrent neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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