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

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.

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), Article 20. https://doi.org/10.3390/electronics7020020

• 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
• Contract Date: Mar 7, 2018