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TECHNICAL PAPERS

Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation

[+] Author and Article Information
Shuhui Li

Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville TX 78363e-mail shuhui.li@tamuk.edu

Donald C. Wunsch

Department of Electrical and Computer Engineering, University of Missouri-Rolla, Rolla MO 65409

Edgar O’Hair, Michael G. Giesselmann

Department of Electrical Engineering, Texas Tech University, Lubbock TX 79409

J. Sol. Energy Eng 123(4), 327-332 (Jul 01, 2001) (6 pages) doi:10.1115/1.1413216 History: Received January 01, 2001; Revised July 01, 2001
Copyright © 2001 by ASME
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References

Joensen, A., Madsen, H., and Nielsen, T. S., 1997, “Non-Parametric Statistical Methods for Wind Power Prediction,” presented at EWEC’97, Dublin, Denmark.
Landberg, L., 1997, “A Mathematical Look at a Physical Power Prediction Model,” presented at EWEC’97, Dublin, Denmark.
Kariniotakis,  G. N., Stavrakakis,  G. S., and Nogaret,  E. F., 1996, “Wind Power Forecasting using Advanced Neural Networks Models,” IEEE Trans. on Energy Conversion, 11, No. 4, pp. 762–767.
Bossanyi, E. A., 1985, “Stochastic Wind Prediction for Wind Turbine System Control,” Proc. of 7th British Wind Energy Association Conf. Oxford, U.K., pp. 219–226.
Li, S., O’Hair, E., and Giesselmann, M., 1997, “Using neural networks to predict wind power generation,” Proc. of Int. Solar Energy Conf. Washington D.C., pp. 415–420.
Walker, J. F., and Jenkins, N., 1997, Wind Turbine Technology, John Wiley & Sons.
American Wind Energy Association, 1988, “Standard Performance Testing of Wind Energy Conversion Systems,” AWEA Standard, AWEA 1.1.
Frost, W., and Aspliden, C., 1995, “Characteristics of the Wind,” in Wind Turbine Technology, David A. Spera (ed.), ASME Press, pp. 371–445.
Krause,  P. C., and Man,  D. T., 1981, “Dynamic Behavior of a Class of Wind Turbine Generators during Electrical Disturbances,” IEEE Trans. Power Appar. Syst., PAS-100, No. 5, pp. 2204–2210.
Allen, D. M., and Cady, F. B., 1982, Analyzing Experimental Data by Regression, Lifetime Learning Publications.
Draper, N. R., and Smith, H., 1981, Applied Regression Analysis, John Wiley & Sons.
Haykin, S. S., 1994, Neural Networks: A Comprehensive Foundation, Macmillan.
Hush, D. R., and Horne, B., 1993, “Progress in Supervised Neural Networks,” IEEE Signal Process. Mag., pp. 1–38.
Kosmatopoulos,  E. B., Polycarpou,  M. M., Christodoulo,  M. A., and Ioannou,  P. A., 1995, “High-Order Neural Network Structures for Identification of Dynamic Systems,” IEEE Trans. Neural Netw., 6, No. 2, pp. 422–431.
Thimm,  G., and Fiesler,  E., 1997, “High-Order and Multilayer Perception Initialization,” IEEE Trans. Neural Netw., 8, No. 2, pp. 349–359.
Zhu, H., and Rohwer, R., 1997, “Measurements of Generalisation Based on Information Geometry,” in Mathematics of Neural Networks: Models, Algorithms and Applications, S. W. Ellacott, J. C. Mason, and I. J. Anderson (eds.), pp. 394–398.
Singhal, S., and Wu, L., 1989, “Training Multilayer Perceptrons with the Extended Kalman Filter Algorithm,” in Advances in Neural Information Processing Systems, Morgan Kaufmann, San Mateo, CA, pp. 133–140.
Kumar, V., Grama, A., Gupta, A., and Karypis, G., 1999, Introduction to Parallel Computing, Benjamin/Cummings, pp. 151–196.
Saratchandran, P., Sundararajan, N., and Foo, S., 1996, Parallel Implementations of Backpropagation Neural Networks on Transputers, World Scientific.
Puskorius, G. V., and Feldkamp, L. A., 1991, “Decoupled Extended Kalman Filter Training of Feedforward Layered Networks,” in Proc. of Int. Joint Conf. on Neural Networks, Seattle WA, pp. 771–777.

Figures

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Central and South West renewable project small wind farm, Fort Davis, Texas
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Wind power generation vs. wind speed (turbine No. 6, March 1996)
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Wind rose diagram on Fort Davis wind farm (Roxal, 40m level, 5/1/94 to 4/30/95). It shows probability distribution of wind directions in a circle
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Structure of the multilayered perceptron network with single hidden layer (Node 0 is bias.)
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Using wind speed of met. 18 to estimate that of met. 19 (March 1996)
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Using wind speed of met. 18 to estimate that of met. 19 (April 1996) (shows the scatter plot of the measured wind speeds, the prediction by a linear regression model, and the prediction by neural network model)
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Wind turbine power prediction by polynomial regression models for test data (turbine No. 6, April 1996)
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Comparison of wind turbine power prediction by 3rd order regression and neural network models for test data (turbine No. 6, April 1996)
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Comparison of RMS errors for training a high-order and the first-order neural networks. For the high-order network, the network inputs includes 1st, 2nd, and 3rd orders of wind speeds, and two products of wind speeds with transformed wind directions, i.e., the same items as those in Eq. (8).

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