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Published in:
Solving Large Dynamical Systems by Constraint Sampling Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Control of a Microturbine Using Neural Networks

Authors : Helbert Espitia, Iván Machón, Hilario López

Published in: Applied Computer Sciences in Engineering

Publisher: Springer International Publishing

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Abstract

In many distributed generation applications, microturbines are used for energy conversion. On the other hand, neural networks are a suitable option for the control of complex non-linear systems. Thus, in this article is shown the speed control of a microturbine using neural networks. For this process, the identification of the microturbine using a neural network is carried out in order to subsequently perform the optimization of the other neural network used for control.

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Literature
1.
Dugan, R., McDermott, T., Ball, G.: Planning for distributed generation. IEEE Ind. Appl. Mag. 7, 80–88 (2001)CrossRef
2.
Tong, J.P., Yu, T.: Nonlinear PID control design for improving stability of micro-turbine systems. In: Electric Utility Deregulation and Restructuring and Power Technologies, pp. 2515–2518 (2008)
3.
Jiayi, H., Chuanwen, J., Rong, X.: A review on distributed energy resources and MicroGrid. Renew. Sustain Energy Rev. 12, 2472–2483 (2008)CrossRef
4.
Khorshidi, A., Zolfaghari, M., Hejazi, M.A.: Dynamic modeling and simulation of microturbine generating system for stability analysis in microgrid networks. Int. J. Basic Sci. Appl. Res. 3(9), 663–670 (2014)
5.
Yu, T., Tong, J.P.: Auto disturbance rejection control of microturbine system. In: IEEE Power Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century (2008)
6.
Li, Z., Wang, D.H., Xue, Y.L.: Research on ways of modeling of microturbine (part 1): analysis of dynamic characteristic. Chin. J. Power Eng. 25, 13–17 (2005)
7.
Lasseter R.: Dynamic models for micro-turbines and fuel cells. In: Power Engineering Society Summer Meeting, pp. 761–766 (2001)
8.
Rowen, W.J.: Simplified mathematical representation of heavy duty gas turbines. ASME J. Eng. Power 105, 865–869 (1983)CrossRef
9.
Nguyen, H., Prasad, N., Walker, C., Walker, E.: A First Course in Fuzzy and Neural Control. Chapman & Hall/CRC, Boca Raton (2003)MATH
10.
Jurado, F.: Modeling micro-turbines using Hammerstein models. Int. J. Energy Res. 29, 841–85 (2005)CrossRef
11.
Guda, S., Wang, C., Nehrir, M.: Modeling of microturbine power generation systems. Electr. Power Compon. Syst. 34(9), 1027–1041 (2006)CrossRef
12.
Wanik, M., Erlich, I.: Dynamic simulation of microturbine distributed generators integrated with multi-machines power system network. In: 2nd IEEE International Conference on Power and Energy, PECon 2008 (2008)
13.
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)
14.
Rolan, A., Luna, A., Gerardo, V., Aguilar, D., Azevedo, G.: Modelling of a variable speed wind turbine with a permanent magnet synchronous generator. In: IEEE International Symposium on Industrial Electronics (ISIE), pp. 734–739, 5–8 July 2009
15.
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), pp. 17–18 (2014)
16.
Espitia, H., Díaz, G.: Identification of a permanent magnet synchronous generator using neuronal networks. In: Workshop on Engineering Applications - International Congress on Engineering (WEA) (2015)
17.
18.
Torres, N., Hernandez, C., Pedraza, L.: Redes neuronales y predicción de tráfico. Revista Tecnura 15(29), 90–97 (2011)
19.
Martinez, F., Gómez, D., Castiblanco, M.: Optimization of a neural architecture for the direct control of a Boost converter. Revista Tecnura 16(32), 41–49 (2012)CrossRef
20.
Pérez, F., Fernández, H.: Las redes neuronales y la evaluación del riesgo de crédito. Revista Ingenierpias Universidad de Medellín 6(10), 77–91 (2007)
21.
Arrieta, J., Torres, J., Velásquez, H.: Predicciones de modelos econométricos y redes neuronales: el caso de la acción de SURAMINV. Semestre Económico 12(25), 95–109 (2009)
22.
Heaton, J.: Introduction to Neural Networks with Java, pp. 41–49. Heaton Research Inc., St. Louis (2008)
23.
Hagan, M., De Jesús, O., Schultz, R.: Training recurrent networks for filtering and control. In: Medsker, L.R., Jain, L.C. (eds.) Recurrent Neural Networks: Design and Applications, pp. 325–354. CRC Press, Boca Raton (2000)
24.
De Jesús, O., Hagan, M.T.: Backpropagation algorithms for a broad class of dynamic networks. IEEE Trans. Neural Netw. 18(1), 14–27 (2007)CrossRef
25.
Garrido, S.: Identificación, estimación y control de sistemas no-lineales mediante RGO. Tesis para optar al título de Doctor, Departamento de Ingeniería de Sistemas y Automática, Universidad Carlos III, Madrid, España (1999)
26.
Singh, M., Singh, I., Verma, A.: Identification on non linear series-parallel model using neural network. MIT Int. J. Electr. Instrum. Eng. 3(1), 21–23 (2013)
27.
Li, Y., Zhao, X., Jiao, L.: A nonlinear system identification approach based on neurofuzzy networks. In: Proceedings of ICSP (2000)
Metadata
Title
Control of a Microturbine Using Neural Networks
Authors
Helbert Espitia
Iván Machón
Hilario López
Copyright Year
2019
Book
Applied Computer Sciences in Engineering

Print ISBN: 978-3-030-31018-9

Electronic ISBN: 978-3-030-31019-6

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-31019-6_18

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