Optimum Setting Strategy for WTGS by Using an Adaptive Neuro-Fuzzy Inference System

Abstract

With the popularization of wind energy, the further reduction of power generation cost became the critical problem. As to improve the efficiency of control for variable speed Wind Turbine Generation System (WTGS), the data-driven Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to establish a sensorless wind speed estimator. Moreover, based on the Supervisory Control and Data Acquisition (SCADA) System, the optimum setting strategy for the maximum energy capture was proposed for the practical operation process. Finally, the simulation was executed which suggested the effectiveness of the approaches.

Share and Cite:

Y. Hu, J. Liu and Z. Lin, "Optimum Setting Strategy for WTGS by Using an Adaptive Neuro-Fuzzy Inference System," Energy and Power Engineering, Vol. 5 No. 4B, 2013, pp. 404-408. doi: 10.4236/epe.2013.54B078.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] “Technology Roadmap: Wind Energy,” International Energy Agency, 2009. http://www.iea.org/publications/freepublications/publication/Wind_Roadmap.pdf
[2] “World Energy Outlook 2012,” International Energy Agency, 2012. http://www.iea.org/publications/freepublications/publication/English.pdf
[3] S. Bhowmik, R. Spee, and J. H. R. Enslin, “Performance Optimization for Doubly Fed Wind Power Generation Systems,” IEEE Transactions on Industry Applications, Vol. 35, No. 4, 1999, pp. 949-958. doi:10.1109/28.777205
[4] K. Tan and S. Islam, “Optimal Control Strategies in Energy Conversion of PMSG Wind Turbine System without Mechanical Sensors,” IEEE Transactions on Energy Conversion, Vol. 19, No. 2, 2004, pp. 392-399. doi:10.1109/TEC.2004.827038
[5] M. G. Simoes, B. K. Bose and R. J. Spiegel, “Fuzzy Logic Based Intelligent Control of a Variable Speed Cage Machine Wind Generation System,” IEEE Transactions on Power Electronics, Vol. 12, No. 1, 1997, pp. 87-95. doi:10.1109/63.554173
[6] H. Li, K. L. Shi and P. G. McLaren, “Neural-Network- Based Sensorless Maximum Wind Energy Capture with Compensated Power Coefficient,” IEEE Transactions on Industry Applications, Vol. 41, No. 6, 2005, pp. 1548- 1556. doi:10.1109/TIA.2005.858282
[7] V. Calderaro, V.Galdi, A.Piccolo and P.Siano, “A Fuzzy Controller for Maximum Energy Extraction from Variable Speed Wind Power Generation Systems,” Electric Power Systems Research, Vol. 78, No. 6, 2008, pp. 1109-1118. doi:10.1016/j.epsr.2007.09.004
[8] V. Galdi, A. Piccolo and P.Siano, “Exploiting Maximum Energy from Variable Speed Wind Power Generation Systems by Using an Adaptive Takagi-Sugeno-Kang fuzzy Model,” Energy Conversion and Management, Vol. 50, No. 2, 2009, pp. 413-421. doi:10.1016/j.enconman.2008.09.004
[9] T. Takagi and M. Sugeno, “Fuzzy Identification of Systems and Its Applications to Modeling and Control,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 15, No. 1, 1985, pp. 116-132. doi:10.1109/TSMC.1985.6313399
[10] J. J-SR, “ANFIS Adaptive-network-based Fuzzy Inference Systems,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 23, No. 3, 1993, pp. 665-85. doi:10.1109/21.256541
[11] X. N. Yu, F. Z. Cheng, L. L. Zhu and Y. J. Wang, “ANFIS Modeling Based on T-S Model and Its Application for Thermal Process,” Proceedings of the CSEE, Vol. 26, No. 15, 2006, pp. 78-82.
[12] A. D. Hansen, P. Sørensen, F. Iov and F. Blaabjerg, “Control of Variable Speed Wind Turbines with Doubly-fed Induction Generators,” Wind Engineering, Vol. 4, No. 28, 2004, pp. 411-432. doi:10.1260/0309524042886441
[13] S. Heier and R. Waddington, “Grid Integration of Wind Energy Conversion System,” 2nd Edition, Wiley, West Sussex, 2006.
[14] S. S. Haykin, “Neural networks: A Comprehensive Foundation,” 2nd Edition, Prentice Hall, USA, 1999.
[15] M. T. Hagan, H. B. Demuth and M. Beale, “Neural Network Design,” 1st Edition, Thomson Learning, Boston, 1996.
[16] R. Y. Ronald and P. F. Dimitar, “Approximate Clustering via the Mountain Method,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 24, No. 8, 1994, pp.1279-1284. doi:10.1109/21.299710
[17] J. Wolberg, “Data Analysis Using the Method of Least Squares: Extracting the Most Information from Experiments,” 1st Edition, Springer, Germany, 2005.
[18] T. Strutz, “Data Fitting and Uncertainty (a practical introduction to Weighted Least Squares and Beyond),” 1st Edition, Vieweg Teubner, Germany, 2010.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.