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| Wind Power Slope Events Classification and Forecasting Based on Statistical Analysis and Multiple Support Vector Machines |
| LI Fu-Dong-1, 2 , WU Min-1, FENG Gao-Yi-3 |
| （1. School of Information Science and Engingeering, Hunan Engineering Laboratory for Advanced Control and Intelligent Automation, Central South University, Changsha 410083, China; 2. The Training Center of Hunan Electric Power Corporation, Changsha 410131, China;3. The New Energy Company of Datang Huayin, Shaoyang 422000, Hunan, China） |
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Abstract To evaluate the influence of wind power fluctuations and optimize the control of wind power system, a method of wind power slope events classification and forecasting based on statistical analysis and multiple support vector machine was presented. Firstly, the wind power slope events were defined and classified. Then, the wind power data collected from a wind farm were used to investigate the classification and range of slope events, and the internal laws of slope events were explored. In this context, the binary support vector machine(SVM) was extended to multiple support vector machines(MSVMs) and was applied to the classification of slope down/up events for both one-step and multi-step ahead scenarios. Finally, the numerical results based on the wind power data verify the effectiveness of the proposed approach.
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Received: 20 June 2012
Published: 29 December 2012
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