Modified Neural Network for Dynamic Control and Operation of a Hybrid Generation Systems

 

Cong-Hui Huang

 

Department of Automation and Control Engineering, Far East University *ch_huang@cc.feu.edu.tw

 

Abstract

This paper presents modified neural network for dynamic control and operation of a hybrid generation systems. PV and wind power are the primary power sources of the system to take full advantages of renewable energy, and the diesel-engine is used as a backup system. The simulation model of the hybrid system was developed using MATLAB Simulink. To achieve a fast and stable response for the real power control, the intelligent controller consists of a Radial Basis Function Network (RBFN) and an modified Elman Neural Network (ENN) for maximum power point tracking (MPPT). The pitch angle of wind turbine is controlled by ENN, and the PV system uses RBFN, where the output signal is used to control the DC / DC boost converters to achieve the MPPT. And the results show the hybrid generation system can effectively extract the maximum power from the PV and wind energy sources.

Keywords: Photovoltaic system, radial basis function network, Elman neural network, maximum power point tracking, diesel-engine.

 

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Acknowledgments

The author would like to thank the National Science Council of the Republic of China, Taiwan for financially supporting the research under Contract No. NSC 102-2221-E-269-018.

 

References

[1] G. Abad, M. A. Rodriguez, G. Iwanski, and J. Poza, "Direct power control of doubly-fed-induction-generator-based wind turbine under unbalanced grid voltage", IEEE Transactions on Power Electron., vol. 25, pp. 442-452, 2010.         [ Links ]

[2] M. Jazaeri, A. A. Samadi, H. R. Najaf, N. Noroozi-Varcheshme, "Eigenvalue Analysis of a Network Connected to a Wind Turbine Implemented with a Doubly-Fed Induction Generator (DFIG)", Journal of Applied Research and Technology, vol. 10, pp. 791-811, 2012.         [ Links ]

[3] A. J. Rudell, J. A. M. Bleijs, L. Freris, D. G. Infield, and G. A. Smith, "A wind diesel system with variable speed flywheel storage", Wind Engineering, vol. 17, pp. 129-145, 1993.         [ Links ]

[4] R. Dettmer, "Revolutionary energy-A wind/diesel generator with flywheel storage", IEE Review, vol. 36, pp. 149-151, 1990.         [ Links ]

[5] Z. Chen, and Y. Hu, "A hybrid generation system using variable speed wind turbines and diesel units", Proceeding of the 29th Annual Conference of the IEEE Industrial Electronics Society, vol. 3, pp. 2729-2734, 2003.         [ Links ]

[6] B. S. Borowy, and Z. M. Salameh, "Dynamic response to a stand-alone wind energy conversion system with battery energy storage to a wind gust", IEEE Transaction on Energy Conversion, vol. 12, pp. 73-78, 1997.         [ Links ]

[7] N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, "Optimization of perturb and observe maximum power point tracking method", IEEE Transaction on Power Electronic, vol. 20, pp. 963-739, 2005.         [ Links ]

[8] B. Yang, Y. Zhao, and X. He, "Design and analysis of a grid-connected photovoltaic power system", IEEE Transaction on Power Electronic, vol. 25, pp. 992-1000, 2010.         [ Links ]

[9] S. Seshagiri, and H. K. Khail, "Output feedback control of nonlinear systems using RBF neural networks", IEEE Transaction on Neural Network, vo1. 11, pp. 69-79, 2000.         [ Links ]

[10] IEEE Std 421.5TM-2005, IEEE Recommended Practice for Excitation System Models for Power System Stability studies, IEEE Power Engineering Society, April 2006.         [ Links ]

[11] S. Liu, and R. A. Dougal, "Dynamic multiphysics model for solar array", IEEE Transaction on Power Electronic., vol. 17, pp. 285-294, 2002. Modified Neural Network for Dynamic Control and Operation of a Hybrid Generation Systems, Cong-Hui Huang/ 1154-1164.

[12] D. Das, R. Esmaili, X. Longya, and D. Nichols, "An optimal design of a grid connected hybrid wind/photovoltaic/fuel cell system for distributed energy production", Proceeding of the 31th Annual Conference of the IEEE Industrial Electronics Society, November 2005, pp. 2499-2504.         [ Links ]

[13] J. L. Pérez, A. Garcés, F. Cabiedes, A. Miranda, "Electronic model of a dubois fuzzy integration neuron", Journal of Applied Research and Technology, vol. 7, pp. 73-82, 2009.         [ Links ]

[14] S. Wan, H. Li, and Y. Li, "Adaptive radial basis function network and Its application in turbine-generator vibration fault diagnosis", Proceeding of the International Conference on Power System Technology, vol. 3, pp. 1607-1610, 2002.         [ Links ]

[15] H. Liu, S. Wang, and P. Ouyang, "Fault diagnosis based on improved Elman neural network for a hydraulic servo system", Proceeding of IEEE Conference on Robotics, Automation and Mechatronics, December 2006, pp. 1-6.         [ Links ]

[16] F. J. Lin, and Y. C. Hung, "FPGA based Elman neural network Control System for Linear Ultrasonic Motor", IEEE Transaction on Ultrasonics Ferroelectrics and Frequency Control, vol. 56, pp. 101-113, 2009.         [ Links ]

[17] J. L. Elman, "Finding structure in time", Cognitive Science, vo1. 14, pp. 179-211, 1990.         [ Links ]