Short-Term Wind Power Forecasting Based on SVM with Backstepping Wind Speed of Power Curve

Abstract:

Article Preview

Accurate wind farm power prediction can relieve the disadvantageous impact of wind power plants on power systems and reduce the difficulty of the scheduling of power dispatching department. Improving accuracy of short-term wind speed prediction is the key of wind power prediction. The authors have studied the short-term wind power forecasting of power plants and proposed a model prediction method based on SVM with backstepping wind speed of power curve. In this method, the sequence of wind speed that is calculated according to the average power of the wind farm operating units and the scene of the power curve is the input of the SVM model. The results show that this method can meet the real-time needs of the prediction system, but also has better prediction accuracy, is a very valuable short-term wind power prediction method.

Info:

Periodical:

Edited by:

Jing Guo

Pages:

401-405

Citation:

X. Y. Yang et al., "Short-Term Wind Power Forecasting Based on SVM with Backstepping Wind Speed of Power Curve", Applied Mechanics and Materials, Vol. 224, pp. 401-405, 2012

Online since:

November 2012

Export:

Price:

$38.00

[1] Sideratos G, Hatziargyriou, in: Using radial basis neural networks toestimate wind power production. IEEE Power Engineering Societ General Meeting, Tampa, USA(2007).

DOI: https://doi.org/10.1109/pes.2007.386190

[2] Miranda M S, Dunn R W, in: One-hour-ahead wind speed prediction using a Bayesian methodology. In Proceeding of IEEE Power Engineering Society General Meeting, Bath, UK(2006).

DOI: https://doi.org/10.1109/pes.2006.1709479

[3] X.Y. Yang, Y.Q. Cui, H.S. Zhang Hongsheng, in: Research on modeling of wind turbine based on LS-SVM". In Proceeding of International Conference on Sustainable Power Generation and Supply, Beijing, China(2009).

DOI: https://doi.org/10.1109/supergen.2009.5348180

[4] X.Y. Yang, B.J. Sun, X.F. Zhang, L.X. Li : Short-Term Wind Speed Forecasting Based on SVM With Similar Data. Proceedings of the CSEE Vol. 32(2012), pp.35-41.

[5] L. J Wang, L Dong, X.Z. Liao, Y. Gao: Short-term Power Prediction of a Wind Farm Based on Wavelet Analysis. Proceedings of the CSEE Vol. 29(2009), pp.30-33.

[6] C Liu, G.F. Fan, W.S. Wang, H.Z. Dai: A Combination Forecasting Model for Wind Farm Output Power. Power System Technology Vol. 33(2009), pp.74-79.