Forecasting of Wind Capacity Ramp Events Using Typical Event Clustering Identification

Abstract

With the large-scale integration of wind generation into the power grid, violent wind speed fluctuation will cause wind power ramp events that can affect the safe and stable operation of power systems. In this article, a forecasting method for day-ahead ramp events is proposed based on wind speed event definition and profile analysis. Firstly, event-based K-means (EB-K) clustering is used to preprocess historical wind speed. Typical event indexes, such as change rate, amplitude, and time intervals are then extensively used to describe ramp event characteristics and decrease the computational burden for the following event identification within given intervals. Then, the similarity of wind power event set is obtained through empirical probability estimation of successive history ramp events. Typical event clustering identification (TECI) algorithm based on EB-K clustering, wind capacity events, and event cluster profiles is proposed to search the maximum occurrence probability for historical data with the similarity indicator. Finally, a case study on a practical farm in Hebei, China is used to verify the effectiveness and accuracy of wind capacity ramp event forecasting by using TECI.

Keywords

• Ramp events
• event clustering identification
• wind power uncertainty
• event forecasting