A wind power ramp event detection method based on eigenvalue correction and trend integration

Abstract

Wind power ramp events have the characteristic of small probability and big hazard, so it is significant to improve the recognition rate and accuracy of ramp events for the safe and stable operation of the power grid. In order to improve the efficiency of ramp event detection, a wind power ramp event detection method based on eigenvalue evaluation correction and trend integration is proposed by combining the feature information of ramp events. The original wind power data is extracted with extreme value feature points and corrected with eigenvalue evaluation to achieve the trend feature extraction effect. In order to avoid being affected by small power jitter events, a trend labeling method is used to integrate the correction sequences. The actual wind power data of a wind farm in Xinjiang is used as an example for ramp event detection. The results of the case show that compared with the original swinging door algorithm, the proposed method has both better trend extraction effect and can avoid the influence of power small jitter events, and can more accurately and more completely identify ramp events.