Selection of time window for wind power ramp prediction based on risk model. (15th October 2016)

• Record Type: Journal Article
• Authors: Zha, Xiaoming; Ouyang, Tinghui; Qin, Liang; Xiong, Yi; Huang, Heming
• Is Part Of: Energy conversion and management. Volume 126(2016)
• Page Start: 748
• Page End: 758
• Publication Date: 2016-10-15
• Subjects: Wind power ramp events -- Time window -- The minimum risk model -- Ramp prediction; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2016.08.064
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Ramp prediction is a novel research area, and a prediction approach by taking time windows as prediction units is proposed. Two risk factors representing reliability and precision are used to build a risk model for selecting the optimal time window. Support vector regression model and a swinging door algorithm are used to predict wind power ramp events. Abstract: To reduce the harm of wind power ramp events in advance, an effective wind power ramp prediction system is needed. A new ramp prediction approach utilizing suitable selected time windows as units is proposed in this paper. Two risk factors are defined based on the performance analysis of ramp prediction, and the risk minimization principle is applied to build the risk model. Combining the correlation analysis and the statistical analysis of ramp duration, model constraints are studied. The optimal time window of industrial data is computed for ramp prediction based on a support vector regression model. Four evaluation indicators are chosen to verify that the proposed approach improves the performance of ramp prediction, and that the risk model is effective to select an optimal time window for prediction.