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http://dx.doi.org/10.5370/KIEE.2011.60.4.838

Stable Adaptive On-line Neural Control for Wind Energy Conversion System  

Park, Jang-Hyun (목포대 공대 제어로봇공학과)
Kim, Seong-Hwan (목포대 공대 제어로봇공학과)
Jang, Young-Hak (목포대 공대 제어로봇공학과)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.60, no.4, 2011 , pp. 838-842 More about this Journal
Abstract
This paper proposes an online adaptive neuro-controller for a wind energy conversion system (WECS) that is a highly nonlinear system intrinsically. In real application, to obtain exact system parameters such as power coefficient, many measuring instruments and implementations are required, which is very difficult to perform. This shortcoming can be avoided by introducing neural network in the controller design in this paper. The proposed adaptive neural control scheme using radial-basis function network (RBFN) needs no system parameters to meet control objectives. Combining derivative estimator for wind velocity, the whole closed-loop system is shown to be stable in the sense of Lyapunov.
Keywords
Adaptive neural control; Wind energy conversion system;
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