By Topic

Neural-network-based sensorless maximum wind energy capture with compensated power coefficient

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

3 Author(s)
Hui Li ; Dept. of Electr. & Comput. Eng., Florida A&M Univ., Tallahassee, FL, USA ; Shi, K.L. ; McLaren, P.G.

This paper describes a small wind generation system where neural network principles are applied for wind speed estimation and robust control of maximum wind power extraction against potential drift of wind turbine power coefficient curve. The new control system will deliver maximum electric power to a customer with light weight, high efficiency, and high reliability without mechanical sensors. The concept has been developed and analyzed using a turbine directly driven permanent-magnet synchronous generator (PMSG). In addition, the proposed method is applied to a 15-kW variable-speed cage induction machine wind generation (CIWG) system. The simulation studies of a PMSG small wind generation system and experimental results of a CIWG are provided to verify the validity of the method.

Published in:

Industry Applications, IEEE Transactions on  (Volume:41 ,  Issue: 6 )