High Order Recurrent Neural Control for Wind Turbine with a Permanent Magnet Synchronous Generator

被引：0

作者：

Ricalde, Luis J. ^{[1
]}

Cruz, Braulio J. ^{[1
]}

Sanchez, Edgar N. ^{[2
]}

机构：

[1] UADY, Fac Ingn, Av Ind Contaminantes Perifer Norte Apdo Post 115, Merida, Yucatan, Mexico

[2] CINVESTAV, Unidad Guadalajara, Guadalajara 45091, Jalisco, Mexico

来源：

COMPUTACION Y SISTEMAS | 2010年 / 14卷 / 02期

关键词：

Neural networks; Wind turbine; Permanent magnet synchronous generator; Maximum power control; Lyapunov methodology;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, an adaptive recurrent neural control scheme is applied to a wind turbine with permanent magnet synchronous generator. Due to the variable behavior of wind currents, the angular speed of the generator is required at a given value in order to extract the maximum available power. In order to develop this control structure, a high order recurrent neural network is used to model the turbine-generator model which is assumed as an unknown system; a learning law is obtained using the Lyapunov methodology. Then a control law, which stabilizes the reference tracking error dynamics, is developed using Control Lyapunov Functions. Via simulations, the control scheme is applied to maximum power operating point on a small wind turbine.

引用

页码：133 / 143

页数：11

共 22 条

[1] Baruch I, 2005, COMPUT SIST, V9, P28
[2] Intelligent control of a class of wind energy conversion systems
Chedid, R
Mrad, F
Basma, M
[J]. IEEE TRANSACTIONS ON ENERGY CONVERSION, 1999, 14 (04) : 1597 - 1604
[3] NEURONS WITH GRADED RESPONSE HAVE COLLECTIVE COMPUTATIONAL PROPERTIES LIKE THOSE OF 2-STATE NEURONS
HOPFIELD, JJ
[J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES, 1984, 81 (10): : 3088 - 3092
[4] IOANNOU PA, 1996, ROBUST ADAPTIVE CONT
[5] Jianzhong Zhang, 2007, International Conference on Electrical Machines and Systems 2007, P324
[6] Khalil H. K, 2002, NONLINEAR SYSTEMS
[7] Adaptive output feedback control of nonlinear systems represented by input-output models
Khalil, HK
[J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1996, 41 (02) : 177 - 188
[8] Dynamical neural networks that ensure exponential identification error convergence
Kosmatopoulos, EB
Christodoulou, MA
Ioannou, PA
[J]. NEURAL NETWORKS, 1997, 10 (02) : 299 - 314
[9] Overview of different wind generator systems and their comparisons
Li, H.
Chen, Z.
[J]. IET RENEWABLE POWER GENERATION, 2008, 2 (02) : 123 - 138
[10] Narendra K S, 1990, IEEE Trans Neural Netw, V1, P4, DOI 10.1109/72.80202

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