Next Article in Journal
A Simple and Efficient Adaptive ISM-Band Antenna Based on a Reconfigurable Optically Driven Parasitic Structure
Previous Article in Journal
RFID 3D-LANDMARC Localization Algorithm Based on Quantum Particle Swarm Optimization
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Electronics
Volume 7
Issue 2
10.3390/electronics7020020
electronics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Neural Network Based Maximum Power Point Tracking Control with Quadratic Boost Converter for PMSG—Wind Energy Conversion System

by
Ramji Tiwari
1,
Kumar Krishnamurthy
1,
Ramesh Babu Neelakandan
1,*,
Sanjeevikumar Padmanaban
2 and
Patrick William Wheeler
3
1
School of Electrical Engineering, VIT University, Vellore 632014, India
2
Department of Energy Technology, Aalborg University, Esbjerg 6700, Denmark
3
Power Electronics and Motion Control (PEMC) Group, Department of Electrical and Electronics Engineering, Nottingham University, Nottingham, NG7 2RD, UK
*
Author to whom correspondence should be addressed.
Electronics 2018, 7(2), 20; https://doi.org/10.3390/electronics7020020
Submission received: 22 December 2017 / Revised: 29 January 2018 / Accepted: 5 February 2018 / Published: 9 February 2018
Browse Figures
Versions Notes

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.
Keywords: DC/DC converter; SEPIC converter; MPPT; RBFN; neural networks; permanent magnet synchronous generator DC/DC converter; SEPIC converter; MPPT; RBFN; neural networks; permanent magnet synchronous generator

Share and Cite

MDPI and ACS Style

Tiwari, R.; Krishnamurthy, K.; Neelakandan, R.B.; Padmanaban, S.; Wheeler, P.W. Neural Network Based Maximum Power Point Tracking Control with Quadratic Boost Converter for PMSG—Wind Energy Conversion System. Electronics 2018, 7, 20. https://doi.org/10.3390/electronics7020020

AMA Style

Tiwari R, Krishnamurthy K, Neelakandan RB, Padmanaban S, Wheeler PW. Neural Network Based Maximum Power Point Tracking Control with Quadratic Boost Converter for PMSG—Wind Energy Conversion System. Electronics. 2018; 7(2):20. https://doi.org/10.3390/electronics7020020

Chicago/Turabian Style

Tiwari, Ramji, Kumar Krishnamurthy, Ramesh Babu Neelakandan, Sanjeevikumar Padmanaban, and Patrick William Wheeler. 2018. "Neural Network Based Maximum Power Point Tracking Control with Quadratic Boost Converter for PMSG—Wind Energy Conversion System" Electronics 7, no. 2: 20. https://doi.org/10.3390/electronics7020020

APA Style

Tiwari, R., Krishnamurthy, K., Neelakandan, R. B., Padmanaban, S., & Wheeler, P. W. (2018). Neural Network Based Maximum Power Point Tracking Control with Quadratic Boost Converter for PMSG—Wind Energy Conversion System. Electronics, 7(2), 20. https://doi.org/10.3390/electronics7020020

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop