Application of Time Series Forecasting Algorithm via Support Vector Machines to Power System Wide-area Stability Prediction

With the development of wide-area measurement technology, it will open up new possibilities for power system protection and control. In this paper we put forward a novel time series forecasting algorithm via support vector machine (SVM), which utilizes synchronized phasor data to provide fast transient stability swings prediction for the use of emergency control. Basic theory analysis of support vector regression in time series forecasting is minutely introduced and a multi-step forecasting formula of generator rotor angles is presented. Final Prediction Error principle is suggested to select the embedding dimension of the forecasting model. Compared with traditional autoregressive forecasting method, SVM adopts the new type of structural risk minimization principle, so it owns excellent generalization ability. The proposed approach has been tested on the IEEE 39-Bus power system, and the result indicates the effectiveness of such prediction model.