文档介绍：Energy Conversion and Management xxx (2010) xxx–xxx
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Energy Conversion and Management
journal homepage: ate/enconman
Design of intelligent controllers for wind generation system with sensorless
maximum wind energy control
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Whei-Min Lin a, Chih-Ming Hong a, , Fu-Sheng Cheng b
a Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan, ROC
b Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung 83305, Taiwan, ROC
article info abstract
Article history: This paper presents the design of an on-line training recurrent fuzzy work (RFNN) controller
Received 3 March 2009 with a high-performance model reference adaptive system (MRAS) observer for the sensorless control
Received in revised form 21 January 2010 of a induction generator (IG). The modified particle swarm optimization (MPSO) is adopted in this study
Accepted 4 September 2010
to adapt the learning rates in the back-propagation process of the RFNN to improve the learning capabil-
Available online xxxx
ity. By using the proposed RFNN controller with MPSO, the IG system can work for stand-alone power
application effectively. The proposed output maximization control is achieved without mechanical sen-
Keywords:
sors such as the wind speed or position sensor, and the new control system will deliver maximum electric
Model reference adaptive system
power with light weight, high efficiency, and high reliability. The estimation of the rotor speed is based
Recurrent fuzzy work
Wind turbine on the MRAS control theory. A sensorless vector-control strategy for an IG operating in a grid-connected
Sensorless control variable speed wind energy conversion system can be achieved.
Induction generator Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction work (FNN) possesses advantages from both sides; it
combines the capability of fuzzy reasoning in handling uncertain
The induction machines are re