A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm

Ehsanolah Assareh and Mojtaba Biglari

Renewable and Sustainable Energy Reviews, 2015, vol. 51, issue C, 1023-1037

Abstract: This paper presents a hybrid method for generator torque control in wind turbines. The generator torque control is usually used in lower wind speeds in order to capture the maximum power. In the proposed method, the wind turbine generator torque is regulated using a proportional and integral (PI) controller. In order to tune the PI gains, a radial basis function (RBF) neural network is used. The optimal dataset to train this neural network is provided by the Gravitational Search Algorithm (GSA). A 5MW wind turbine model based on FAST (Fatigue, Aero-dynamics, Structures and Turbulence) software code developed at the US National Renewable Energy Laboratory (NREL) is used to simulate and verify the results. The simulation results show that the proposed method has a good performance.

Keywords: Wind turbine; Torque control; PI controller; Gravitational Search Algorithm (GSA); Radial basis function (RBF) neural network; FAST (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1364032115006814
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:rensus:v:51:y:2015:i:c:p:1023-1037

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/bibliographic
http://www.elsevier. ... 600126/bibliographic

DOI: 10.1016/j.rser.2015.07.034

Access Statistics for this article

Renewable and Sustainable Energy Reviews is currently edited by L. Kazmerski

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