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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

Ramji Tiwari

Kumar Krishnamurthy

Ramesh Neelakandan

Sanjeevikumar Padmanaban

Patrick Wheeler



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
Publication Date Feb 9, 2018
Journal Electronics
Electronic ISSN 2079-9292
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 7
Issue 2
Article Number 20
APA6 Citation Tiwari, R., Krishnamurthy, K., Neelakandan, R., Padmanaban, S., & Wheeler, P. (2018). Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system. Electronics, 7(2), https://doi.org/10.3390/electronics7020020
DOI https://doi.org/10.3390/electronics7020020
Publisher URL http://www.mdpi.com/2079-9292/7/2/20
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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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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