Transient stability assessment of a large actual power system using least squares support vector machine with enhanced feature selection

This paper presents transient stability assessments of a large actual power system using the least squares support vector machine (LS-SVM) with enhanced feature selection method. The investigated large power system is divided into five smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the amount of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulations carried out by considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the LS-SVM. An enhanced feature selection method is then implemented to reduce the input features to the LS-SVM which is used as a classifier to determine whether the power system is stable or unstable. It can be concluded that the LS-SVM with enhanced feature selection method reduces the time taken to train the LS-SVM without affecting the accuracy of the classification results.