Ramp events forecasting based on long‐term wind power prediction and correction. Issue 15 (23rd October 2019)

• Record Type: Journal Article
• Authors: Ouyang, Tinghui; Huang, Heming; He, Yusen
• Is Part Of: IET renewable power generation. Volume 13:Issue 15(2019)
• Page Start: 2793
• Page End: 2801
• Publication Date: 2019-10-23
• Subjects: wind power plants -- wind power -- weather forecasting -- load forecasting -- data mining -- power engineering computing; ramp events forecasting -- long‐term wind power prediction -- power system -- wind power fluctuation -- advanced ramp prediction approach -- event detection framework -- wind power forecasting -- ramp detection -- high‐performance ramp prediction -- accurate wind power prediction results -- hybrid prediction model -- wind power curve -- wind power generation -- numerical weather prediction system -- long‐term trend prediction -- primary prediction -- long‐term prediction performance -- corresponding ramp prediction -- actual wind dataset; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2019.0093
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : 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.