Abstract
An improved Support Vector Machine (SVM) algorithm is used to forecast wind in Doubly Fed Induction Generator (DFIG) wind power system without aerodromometer. The mathematical model is built after analyzing the principle of wind forecasting with Maximum Power Point Tracing (MPPT), and its kernel functions of SVM is selected. Compares the rapidity and accuracy of parameter optimization methods, we know that the Particle Swarm Optimization (PSO) method is better than the Cross Validation (CV) method. Finally, 3.6MW DFIG wind power system simulation model with wind speed forecasting is established. Simulation results show that the accuracy rate thought improved SVM forecasting algorithm can reach 98.667%, the DFIG system can operate at the MPPT. The whole performance has well robustness and rapidity.
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© 2011 Springer-Verlag Berlin Heidelberg
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Zhang, H., Wang, X., Wu, Y. (2011). Application of an Improved SVM Algorithm for Wind Speed Forecasting. In: Zeng, D. (eds) Future Intelligent Information Systems. Lecture Notes in Electrical Engineering, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19706-2_43
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DOI: https://doi.org/10.1007/978-3-642-19706-2_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19705-5
Online ISBN: 978-3-642-19706-2
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