Wind Power Estimation Using Artificial Neural Network
Publication: Journal of Energy Engineering
Volume 133, Issue 1
Abstract
Wind energy conversion systems appear as an attractive alternative for electricity generation. To maximize the use of wind generated electricity when connected to the electric grid, it is important to estimate and predict power produced by wind farms. The power generated by electric wind turbines changes rapidly because of the continuous fluctuation of wind speed and wind direction. Wind power can be affected by many other factors such as terrain, air density, vertical wind profile, time of a day, and seasons of a year and usually fluctuates rapidly, imposing considerable difficulties on the management of combined electric power systems. It is important for the power industry to have the capability to perform this prediction for diagnostic purposes—lower than expected wind power may be an early indicator of a need for maintenance. A multilayer perceptron (MLP) network can be used to estimate wind turbine power generation. It is usually important to train a neural network with multiple influence factors and big training data set. The extended Kalman filter training algorithm has to be parallelized so that it can provide fast training even for large training data sets. The MLP network can then be trained with the consideration of various possible factors, which can cause influence on turbine power production.
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References
American Wind Energy Association (AWEA). (1998). “Standard performance testing of wind energy conversion systems.” AWEA Standard 1.1.
Blank, T. (1990). “The MasPar MP-1 architecture.” Proc., IEEE CompCon.
Divone, L. V. (1995). Evolution of modern wind turbines in wind turbine technology, AMSE Press, Chap. 3, 73–138.
Feldkamp, L. A., and Puskorius, G. V. (1994). “Training controller for robustness: Multi-stream DEKF.” IEEE Int. Conf. on Neural Network, 2377–2382.
Frost, W., and Aspliden, C. (1995). Characteristics of the wind in wind turbine technology, D. A. Spera, ed., AMSE Press, Chap. 8, 371–445.
Grewal, M. S., and Addrews, A. P. (1993). Kalman filtering: Theory and practice, PrenticeowpiHall, Englewood Cliffs, N.J.
Haykin, S. (1999). Neural networks a comprehensive foundation, Prentice-Hall, International Editions, Englewood Cliffs, N.J.
Haykin, S. S. (1994). Neural networks, Macmillan, New York.
Kalogirou, S., Neocleous, C., Michaelides, S., and Schizas, C. (1998). “Artificial neural networks for the generation of Isohyets by considering land configuration.” Proc., Engineering Applications of Neural Networks (EANN’98) Conf., Gibraltar, 383–389.
Lewis, F. L. (1986). Optimal estimation with an introduction to stochastic control theory, Wiley, New York.
Mohamed, A. M., Rehman, S., and Halawani, T. O. (1998). “A neural network approach for wind speed prediction.” Renewable Energy, 13(3), 345–354.
Puskorius, G. V., and Feldkamp, L. A. (1981). “Decoupled extendedKalman filter training of feedforward layered networks.” Proc., Int. Joint Conf. on Neural Networks, Seattle, 771–777.
Rojas, R. (1996). Neural networks: A systematic introduction, Springer, Berlin.
Shuhui, L., Wunsch, D. C., O’Hair, E., and Giesselmann, M. G. (1999). “Wind turbine power estimation by neural networks with Kalman filter training on a SIMD parallel machine.” Int. Joint Conf. on Neural Networks IJCNN’99, Vol. 5, 3430–3434.
Singhal, S., and Wu, L. (1989). “Training multilayer perceptrons with the extended Kalman filter algorithm.” Advances in neural information processing systems, Morgan Kaufmann, San Mateo, Calif., 133–140.
SPSS Inc. (1999). SPSS-neural connection, release 2.1, Recognition System Ltd.
Venayagamoorthy, G. K., and Harley, R. G. (1999). “Implementation of an adaptive neural network identifier for effective control of turbo generators.” Proc., IEEE Power Tech ’99 Conf., Budapest, Hungary.
Weinberg, C. J., and Ancona, D. F. (1995). “A utility perspective of wind energy in wind turbine technology.” D. A. Spera, ed., AMSE Press, Chap. 13, 589–602.
Information & Authors
Information
Published In
Journal of Energy Engineering
Volume 133 • Issue 1 • March 2007
Pages: 46 - 52
Copyright
© 2007 ASCE.
History
Received: Jun 18, 2004
Accepted: Mar 15, 2005
Published online: Mar 1, 2007
Published in print: Mar 2007
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