Wind power ramp events are typical harmful anomaly events in wind engineering, which bring new threat to the safety operation of power systems. To in-depth understand ramps and mitigate their harms, suitable ramp characteristics are crucial in many studies, e.g., ramp definition, classification, prediction and so on. However, due to ramps’ specificity on event feature, more profound characteristics are needed besides basic ramp morphological characteristics. In this paper, an approach for extracting and selecting ramp characteristics is proposed for ramp study. First, according to ramps’ causation on energy change, wavelet transformation is introduced to analyze ramp categories, and used to extract ramp energy characteristics. Then, heuristic feature selection methods are proposed to select ramp characteristics based on specific ramp application contexts. The objective of feature selection is to remove redundant characteristics, and to improve ramp studies’ performance. Finally, combining basic ramp characteristics and wavelet characteristics, ramp studies on category classification and prediction of appointed characteristics are implemented on industrial data. The computational results validate the usefulness of wavelet characteristics, the feasibility of the proposed approach, and that performance of ramp study could be improved by using ramp characteristics in this paper.
An Improved Power System Transient Stability Prediction Model Based on mRMR Feature Selection and WTA Ensemble Learning
