By Topic

Regression function and back-propagation through time training method for wind turbine neural network pitch-controller

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

2 Author(s)
Oonsivilai, A. ; Alternative & Sustainable Energy Res. Unit, Suranaree Univ. of Technol., Nakhon Ratchasima, Thailand ; Greyson, K.A.

Reliable wind turbines operation for production of electrical energy requires a modern control system. In order to obtain a reliable operation, this paper present an effective control system using artificial Recurrent Neural Network (RNN) trained by Sequential Response Surface (statistical method) learning method in a pitch-control, variable speed wind turbines. The main objective is to ensure stability and optimal operation of a variable speed, fixed pitch turbine at all operating points. The turbine was modeled in order to test the designed controller by simulation. Method consideration were based on above rated and below rated operations so as to extract maximum energy. This is achieved by keeping the rotor power coefficient at the maximum level all the time regardless of the wind speed.

Published in:

Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), 2010 International Conference on

Date of Conference:

19-21 May 2010