Publicado

2009-10-01

NEURAL NETWORK BASED SYSTEM IDENTIFICATION OF A PMSM UNDER LOAD FLUCTUATION

Palabras clave:

System, Identification, PMSM, Neural Network, Recurrent Networks. (es)

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Autores/as

  • JABID QUIROGA Profesor asociado, Universidad Industrial de Santander, Bucaramanga- Colombia
  • DAVID CARTES Profesor asociado, Ingeniería Mecánica, Florida State University, Tallahassee, United States
  • CHRIS EDRINGTON Profesor , Ingeniería Eléctrica y Computacional, Florida State University, Tallahassee, United States
A neural network based approach is applied to model a PMSM. A multilayer recurrent network provides a near term fundamental current prediction using as an input the fundamental components of the voltage signals and the speed. The PMSM model proposed can be implemented in a condition based maintenance to perform fault detection, integrity assessment and aging process. The model is validated using a 15 hp PMSM experimental setup. The acquisition system is developed using Matlab®/Simulink® with dSpace® as an interface to the hardware, i.e. PMSM drive system. The model shows generalization capabilities and a satisfactory performance in the fundamental current determination on line under no load and load fluctuations.

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Cómo citar

[1]
J. QUIROGA, D. CARTES, y C. EDRINGTON, «NEURAL NETWORK BASED SYSTEM IDENTIFICATION OF A PMSM UNDER LOAD FLUCTUATION», DYNA, vol. 76, n.º 160, pp. 273–282, oct. 2009, Accedido: 21 de septiembre de 2026. [En línea]. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/13685