Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system

Tiwari, Ramji and Krishnamurthy, Kumar and Neelakandan, Ramesh and Padmanaban, Sanjeevikumar and Wheeler, Patrick (2018) Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system. Electronics, 7 (2). 20/1-20/17. ISSN 2079-9292

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Abstract

This paper proposes an artificial neural network (ANN) based maximum power point tracking (MPPT) control strategy for wind energy conversion system (WECS) implemented with a DC/DC converter. The proposed topology utilizes a radial basis function network (RBFN) based neural network control strategy to extract the maximum available power from the wind velocity. The results are compared with a classical Perturb and Observe (P&O) method and Back propagation network (BPN) method. In order to achieve a high voltage rating, the system is implemented with a quadratic boost converter and the performance of the converter is validated with a boost and single ended primary inductance converter (SEPIC). The performance of the MPPT technique along with a DC/DC converter is demonstrated using MATLAB/Simulink.

Item Type: Article
Schools/Departments: University of Nottingham, UK > Faculty of Engineering > Department of Electrical and Electronic Engineering
Identification Number: https://doi.org/10.3390/electronics7020020
Depositing User: Eprints, Support
Date Deposited: 07 Mar 2018 15:15
Last Modified: 02 Jul 2018 08:40
URI: http://eprints.nottingham.ac.uk/id/eprint/50298

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