Ramji Tiwari
Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system
Tiwari, Ramji; Krishnamurthy, Kumar; Neelakandan, Ramesh; Padmanaban, Sanjeevikumar; Wheeler, Patrick
Authors
Kumar Krishnamurthy
Ramesh Neelakandan
Sanjeevikumar Padmanaban
Professor PATRICK WHEELER pat.wheeler@nottingham.ac.uk
Professor of Power Electronic Systems
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.
Journal Article Type | Article |
---|---|
Acceptance Date | Feb 5, 2018 |
Publication Date | Feb 9, 2018 |
Deposit Date | Mar 7, 2018 |
Publicly Available Date | Mar 7, 2018 |
Journal | Electronics |
Electronic ISSN | 2079-9292 |
Publisher | MDPI |
Peer Reviewed | Peer Reviewed |
Volume | 7 |
Issue | 2 |
Article Number | 20 |
DOI | https://doi.org/10.3390/electronics7020020 |
Public URL | https://nottingham-repository.worktribe.com/output/911113 |
Publisher URL | http://www.mdpi.com/2079-9292/7/2/20 |
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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0
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