A hybrid forecasting method for wind power ramp based on Orthogonal Test and Support Vector Machine (OT-SVM)

Liu, Yonqian and Sun, Ying and Infield, David and Zhao, Yu and Han, Shuang and Yan, Jie (2017) A hybrid forecasting method for wind power ramp based on Orthogonal Test and Support Vector Machine (OT-SVM). IEEE Transactions on Sustainable Energy, 8 (2). pp. 451-457. ISSN 1949-3037 (https://doi.org/10.1109/TSTE.2016.2604852)

[thumbnail of Liu-etal-ITSE-2016-hybrid-forecasting-method-for-wind-power-ramp]
Preview
 Text. Filename: Liu_etal_ITSE_2016_hybrid_forecasting_method_for_wind_power_ramp.pdf
Accepted Author Manuscript
Download (602kB)| Preview

Abstract

In an electric power system with a high penetration of wind power, incoming power ramps pose a serious threat to the power system. To adopt suitable response strategies for wind power ramps, it is important to predict them accurately and in a timely manner. Since power ramps are caused by various factors, their occurrence have irregular characteristics and vary by location, bringing great difficulty in forecasting. To solve this problem, a hybrid forecasting model OT-SVM (Orthogonal Test and Support Vector Machine) was developed in this paper, which combines an orthogonal test with a support vector machine. A novel factor analysis method was established based on the theory of the orthogonal test (OT), and applied to determine the optimal inputs of a support vector machine (SVM). The effectiveness of OT-SVM was tested with three wind farms in China, while comparing the results with other related methods. The results show that the proposed OT-SVM has the highest accuracy covering different input numbers and time resolutions. In addition, a novel evaluation index MAI (Mean Accuracy Index) was proposed, considering both the missed ramps and false ramps, which can be used as a supplementary index for CSI.

CORE (COnnecting REpositories)