Title: Quadratic boost converter for wind energy conversion system using back propagation neural network maximum power point tracking

Authors: Ramji Tiwari; P. Pandiyan; S. Saravanan; T. Chinnadurai; N. Prabaharan; K. Kumar

Addresses: Department of EEE, Sri Krishna College of Engineering and Technology, Coimbatore, India ' Department of EEE, KPR Institute of Engineering and Technology, Coimbatore, India ' Department of EEE, Sri Krishna College of Technology, Coimbatore, India ' Department of Mechatronics, School of Mechanical Engineering, REVA University, Bengaluru, India ' Department of EEE, SASTRA Deemed University, Thanjavur 613401, Tamilnadu, India ' Department of EEE, SV College of Engineering, Tirupati, India

Abstract: The article presents a DC-to-DC high step-up quadratic boost converter with maximum power point tracking (MPPT) technique using artificial neural network (ANN) for wind energy transfer systems. In order to get the maximum possible electrical energy from the wind speed, the proposed topology employs a back propagation network (BPN) based neural network control technique. A quadratic boost converter (QBC) is employed in this system to attain the higher voltage rating, and its performance is tested with a boost converter to determine its efficiency. The proposed system is developed in MATLAB/Simulink platform to demonstrate the operating principle under continuous conduction mode. The results obtained from this proposed system are more favourable as compared with classical perturb and observe (P&O).

Keywords: wind energy conversion system; WECS; quadratic boost converter; QBC; artificial neural network; ANN; maximum power point tracking; MPPT; permanent magnet synchronous generator; PMSG.

DOI: 10.1504/IJETP.2022.121516

International Journal of Energy Technology and Policy, 2022 Vol.18 No.1, pp.71 - 89

Received: 10 Jan 2021
Accepted: 06 Sep 2021
Published online: 16 Mar 2022 *

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