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This paper presents transient stability assessment of a large actual 87-bus system and the IEEE 39-bus system using the probabilistic neural network (PNN) with enhanced feature selection and extraction methods. The investigated power systems are divided into 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 PNN. An enhanced feature selection and extraction methods are then incorporated to reduce the input features to the PNN which is used as a classifier to determine whether the power system is stable or unstable. It can be concluded that the PNN with enhanced feature selection and extraction methods reduces the time taken to train the PNN without affecting the accuracy of the classification results.

Transient Stability Assessment of Power Systems using Probabilistic Neural Network with Enhanced Feature Selection and Extraction