Wind power ramp events had a significant impact on the power balance of power system and may lead to load shedding. A data driven method was proposed for wind power ramp events assessment in this paper. The K-means clustering algorithm was used to divide the samples to several classes. The stacked denoising autoencoder was used to extract layer features to train support vector machine. Historical and forecast data of wind power, load power, conventional unit and pumped storage station power were taken as inputs. The output was whether ramp event occurred. A severity function was constructed to assess the severity grade which was predicted to be a wind power ramp event based on effect theory. The credibility of the assessment result was represented by confidence interval. Simulation results of a provincial power grid showed that the prediction method in this paper was more accurate and credibility was high enough to help the dispatchers to take measures for the security of power grid.

deep learning; K-means clustering algorithm; power system; wind power ramping; severity grading