Abstract
Wind power generation is gaining popularity as the power industry in the world is moving toward more liberalized trade of energy along with public concerns of more environmentally friendly mode of electricity generation. The weakness of wind power generation is its dependence on nature—the power output varies in quite a wide range due to the change of wind speed, which is difficult to model and predict. The excess fluctuation of power output and voltages can influence negatively the quality of electricity in the distribution system connected to the wind power generation plant. In this paper, the authors propose an intelligent adaptive system to control the output of a wind power generation plant to maintain the quality of electricity in the distribution system. The target wind generator is a cost-effective induction generator, while the plant is equipped with a small capacity energy storage based on conventional batteries, heater load for co-generation and braking, and a voltage smoothing device such as a static Var compensator (SVC). Fuzzy logic controller provides a flexible controller covering a wide range of energy/voltage compensation. A neural network inverse model is designed to provide compensating control amount for a system. The system can be optimized to cope with the fluctuating market-based electricity price conditions to lower the cost of electricity consumption or to maximize the power sales opportunities from the wind generation plant.
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References
Karaki, S.H., Chedid, R.B., Ramadan, R.: Probabilistic Production Costing of Diesel-Wind Energy Conversion Systems. IEEE Trans. on Energy Conversion 15, 284–289 (2000)
Pandiaraj, K., Taylor, P., Jenkins, N.: Distributed Load Control Autonomous Renewable Energy Systems. IEEE Trans. on Energy Conversion 16, 14–19 (2001)
Chedid, R.B., Karaki, S.H., Chadi, E.C.: Adaptive Fuzzy Control for Wind-Diesel Weak Power Systems. IEEE Trans. on Energy Conversion 15, 71–78 (2000)
Tripathy, S.C., Kalantar, M., Balasubramanian, R.: Dynamics and Stability of Wind and Diesel Turbine Generator with Superconducting Magnetic Energy Storage Unit on an Isolated Power System. IEEE Trans. on Energy Conversion 6, 579–585 (1991)
Passino, K.M.: Fuzzy Control: Theory and Applications. Addison Wesley Publishing, Reading (1997)
Yen, J., Langari, R.: Fuzzy Logic: Intelligence, Control, and Information. Prentice Hall, Englewood Cliffs (1999)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, New Jersey (1998)
Ng, G.W.: Application of Neural Networks to Adaptive Control of Nonlinear Systems. John Wiley and Sons Inc., Chichester (1997)
Madsen, P.P.: Neural Network for Optimization of Existing Control Systems. In: Proc. IEEE International Joint Conference on Neural Networks, Australia, pp. 1496–1501 (1995)
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© 2008 Springer-Verlag Berlin Heidelberg
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Ko, HS., Kang, MJ., Kim, HC. (2008). Electricity Quality Control of an Independent Power System Based on Hybrid Intelligent Controller. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_49
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DOI: https://doi.org/10.1007/978-3-540-69162-4_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69159-4
Online ISBN: 978-3-540-69162-4
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