By Topic

A robust wind turbine control using a Neural Network based wind speed estimator

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Barambones, O. ; Dept. of Autom. Control & Syst. Eng., Univ. of the Basque Country, Vitoria, Spain

Modern wind turbines are capable to work in variable speed operations. These wind turbines are provided with adjustable speed generators, like the double feed induction generator. One of the main advantage of adjustable speed generators is that they improve the system efficiency compared to fixed speed generators because turbine speed is adjusted as a function of wind speed to maximize output power. In this sense, to implement maximum wind power extraction, most controller designs of the variable-speed wind turbine generators employ anemometers to measure wind speed in order to obtain the desired optimal generator speed. In this paper a Neural Network based wind speed estimator for a wind turbine control is proposed. The design uses a feedforward Artificial Neural Network (ANN) to implement a wind speed estimator. In this work, a sliding mode control for variable speed wind turbines is also proposed. The stability analysis of the proposed controller is provided under disturbances and parameter uncertainties by using the Lyapunov stability theory. Finally simulated results show, on the one hand that the proposed control scheme using an ANN estimator provides high-performance dynamic characteristics, and on the other hand that this scheme is robust under uncertainties that usually appear in the real systems and under wind speed variations.

Published in:

Neural Networks (IJCNN), The 2010 International Joint Conference on

Date of Conference:

18-23 July 2010