Evaluation of classification methods for on-line identification of power system dynamic signature

Abstract

The paper investigates the use of Decision Tree (DT), Ensemble DT and multiclass Support Vector Machine (SVM) for on-line prediction of post-fault system dynamic signature based on Phasor Measurement Unit (PMU) measurements. The performance of these multiclass classification techniques is compared in terms of i) how fast the prediction about generator grouping can be made after the clearance of transient disturbance and ii) the accuracy of prediction. The application of these methods is illustrated on a 16-machine, 68-bus test system. Results indicate that the Ensemble DT method performs the best by achieving accuracy of close to 90% using 10 cycles data of post-disturbance generator rotor angles as predictors and over 90% using 30 cycles data of rotor angles as predictors.