Principal Component Analysis for Evaluation of Wind Ramp Event Probability

During last few decades wind energy has become one of the fastest growing renewable sources. Recent research interest in wind power forecasting community is focused on wind ramps, sudden changes in wind power output. Conventional ramp prediction methods derive future ramp estimates from power forecast series. We suggest to analyze real wind power series or other weather parameters searching for specific patterns and dependencies signalizing forthcoming wind ramp event. In this paper, principal component analysis of wind speed time series is presented. Analyzed power production and wind speed data were collected from August 2011 to July 2012 on sample wind farm located close to Let bridge, AB Canada. Sensitivity of analysis is defined by length of selected time frame. Frequency tables for first three principal components are used to evaluate conditional probability of forthcoming wind ramp event. Advantage of proposed methodology compared to conventional methods is that numerical weather prediction (NWP) model producing wind forecasts is not required.