The wind power ramp event is a small probability event with severe wind power fluctuations, so it is very important to quickly detect the ramp event in big data. In order to improve the detection efficiency of the ramp event, this paper proposes a wind power ramp event detection method based on the combination of SDT and trend marking according to the significant trend information of the ramp event. Firstly, the improved revolving door algorithm (SDT) is used to segment the original wind power data for segmentation trend extraction, and pre-extract the possible ramp events. In order to avoid missed detection and processing unimportant segments, a method of trend marking is introduced. According to the proposed ramp test method, the data of a wind field in Shanghai is tested for ramp. The results show that the segmentation extraction trend of ramp events not only shortens the ramp detection time but also improves the accuracy of ramp detection, which has practical significance.
