Abstract
This document shows a neuro-fuzzy control system to regulate the velocity of a permanent magnet synchronous generator. This scheme comes up with two neuro-fuzzy systems where the first identifies the dynamics of the plant; the second is employed for control purposes. Subsequently, the performed training is examined to different reference values.
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References
Dugan, R., McDermott, T., Ball, G.: Planning for distributed generation. IEEE Ind. Appl. Mag. 7, 80–88 (2001)
Piagi, P., Lasseter, R.: Microgrid: a conceptual solution. In: Proceedings of the Power Electronics Specialists Conference, vol. 6, pp. 4285–4290 (2004)
Chen, J., Wu, H., Sun, M., Jiang, W., Cai, L., Guo, C.: Modeling and simulation of directly driven wind turbine with permanent magnet synchronous generator. In: IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia) (2012)
Patil, k., Mehta, B.: Modeling and simulation of variable speed wind turbine with direct drive permanent magnet synchronous generator. In: International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE) (2014)
Nguyen, H., Prasad, N., Walker, C., Walker, E.: A First Course in Fuzzy and Neural Control. Chapman & Hall/CRC, London (2003)
Espitia, H., González, G.: Identification of a permanent magnet synchronous generator using neuronal networks. In: IEEE Workshop on Engineering Applications (WEA), pp. 1–5 (2015)
Patil, K., Mehta, B.: Modeling and control of variable speed wind turbine with permanent magnet synchronous generator. In: International Conference on Advances in Green Energy (ICAGE), 17–18 December 2014 (2014)
Yin, M., Li, G., Zhou, M., Zhao, C.: Modeling of the wind turbine with a permanent magnet synchronous generator for integration. In: IEEE Power Engineering Society General Meeting, pp. 1–6 (2007)
Li, Y., Zhao, X., Jiao, L.: A nonlinear system identification approach based on neuro-fuzzy Networks. In: Proceedings of ICSP (2000)
Morales, L.: Estudio y evaluación del método de defuzificación basado en relaciones booleanas (DBR) aplicado a las redes neuro-difusas, para la identificación de sistemas no lineales. Proyecto de grado en Ingeniería Electrónica, Universidad Distrital Francisco José de Caldas (2009)
Wang, L.: A Course in Fuzzy Systems and Control. Prentice Hall, Englewood Cliffs (1997)
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Espitia, H., Díaz, G., Díaz, S. (2018). Control of a Permanent Magnet Synchronous Generator Using a Neuro-Fuzzy System. In: Figueroa-García, J., López-Santana, E., Rodriguez-Molano, J. (eds) Applied Computer Sciences in Engineering. WEA 2018. Communications in Computer and Information Science, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-00350-0_8
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DOI: https://doi.org/10.1007/978-3-030-00350-0_8
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