Improving efficiency of two-type maximum power point tracking methods of tip-speed ratio and optimum torque in wind turbine system using a quantum neural network

Soheil Ganjefar, Ali Akbar Ghassemi and Mohamad Mehdi Ahmadi

Energy, 2014, vol. 67, issue C, 444-453

Abstract: In this paper, a quantum neural network (QNN) is used as controller in the adaptive control structures to improve efficiency of the maximum power point tracking (MPPT) methods in the wind turbine system. For this purpose, direct and indirect adaptive control structures equipped with QNN are used in tip-speed ratio (TSR) and optimum torque (OT) MPPT methods. The proposed control schemes are evaluated through a battery-charging windmill system equipped with PMSG (permanent magnet synchronous generator) at a random wind speed to demonstrate transcendence of their effectiveness as compared to PID controller and conventional neural network controller (CNNC).

Keywords: Battery-charging windmill system; Maximum power point tracking; Tip-speed ratio; Optimum torque; Quantum neural network; Direct and indirect adaptive control (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544214001571
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:67:y:2014:i:c:p:444-453

DOI: 10.1016/j.energy.2014.02.023

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().