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

Control neuronal recurrente de alto orden para turbinas de viento con generador síncrono de imán permanente

Luis J. Ricalde¹, Braulio J. Cruz¹ and Edgar N. Sánchez²

¹ UADY, Facultad de Ingeniería, Av. Industrias no Contaminantes por Periférico Norte Apdo. Postal 115 Cordemex, Mérida, Yucatán, México. E–mail: lricalde@uady.mx

² CINVESTAV, Unidad Guadalajara, Apartado Postal 31–430, Plaza La Luna, C.P. 45091, Guadalajara, Jalisco México. E–mail: sanchez@gdl.cinvestav.mx

Article received March 18 2009.
Accepted on 23 September. 2009.

Abstract

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.

Keywords: Neural networks, Wind turbine, Permanent magnet synchronous generator, Maximum power control, Lyapunov methodology.

Resumen

En este artículo un esquema de control adaptable neuronal recurrente es aplicado a una turbina de viento con un generador síncrono de imán permanente. Debido al comportamiento variable de las corrientes de viento, la velocidad angular del generador es requerida a un valor específico para poder extraer la máxima potencia disponible. Para desarrollar la estructura de control, una red neuronal recurrente de alto orden es utilizada para modelar el sistema generador–turbina el cual es considerado desconocido; una ley de aprendizaje es obtenida utilizando el método de Lyapunov. Una ley de control, que estabiliza la dinámica del error de seguimiento de trayectoria es desarrollada utilizando Funciones de Control de Lyapunov. Mediante simulación, el esquema de control es aplicado a un punto de operación de máxima potencia en una turbina de viento de baja potencia.

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Acknowledgements

The first author thanks PROMEP Project PROMEP/103.5/07/2595 for supporting this research. The second author thanks CONACyT, Mexico, Project FOMIX 66192, for supporting this research.

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