SOFM based support vector regression model for prediction and its application in power system transient stability forecasting

This paper proposes a real time prediction method that may be applied in power system transient stability forecasting which can predict the future behavior by using SVR (support vector regression) and the data coming from PMUs (phasor measurement units). With a view to improving the training efficiency of SVR and the prediction accuracy, the proposed method is based on the self-organizing feature map (SOFM) that can discover the similar training input data and cluster them into several classes in which input data have approximate trend. Then, the similar data are used as input data for a SVR predictor. Because the SOFM extracts similar data from learning data as a preprocessor, which decreases the size of the sample set for one SVR, and also reduces the mutual influences of other learning data that are not related to the similar data of one class, the method not only enhances the training speed but also can forecast with high accuracy under different conditions. Forecasting results of simulation on New England 10 generators system prove the feasibility of this model