Ramp events forecasting based on long-term wind power prediction and correction

To mitigate the threat to power system caused by ramp events - large wind power fluctuation, this study proposes an advanced ramp prediction approach based on event detection framework. This approach contains two successive stages of work, including wind power forecasting and ramp detection. Considering high-performance ramp prediction requires long-term and accurate wind power prediction results; this study also proposes a hybrid prediction model at the first stage. By using wind power curve to reflect the physic mechanism of wind power generation, data from numerical weather prediction system could be used to realise long-term trend prediction. Then, a multivariate model is built with a data-mining algorithm to correct system errors of the primary prediction, which is addressed to improve long-term prediction performance. At the second stage, a modified swinging door algorithm is applied for ramp detection. Performance of both the proposed long-term wind power prediction and the corresponding ramp prediction are computed and compared with conventional models on an actual wind dataset. Comprehensive results validated the feasibility and superiority of the proposed ramp prediction approach.