In this paper, a Committee Neural Networks (CNN) is proposed for transient stability prediction. Transient stability of a power system is first determined based on the generator relative rotor angles procured from time domain simulation outputs. Simulations were carried out on the IEEE 9-bus test system considering three phase faults on the system. The data collected from the time domain simulations are then used as inputs to the CNN in which CNN is used as a classifier to determine whether the power system is stable or unstable. To verify the effectiveness of the proposed CNN method, it is compared with the Probabilistic Neural Networks (PNN) and the Multi Layer Perceptrons Neural Networks (MLP). Results show that the CNN gives more accurate transient stability assessment compared to the probabilistic neural network and multi layer perceptrons neural networks in terms of classification results.

Transient Stability Assessment (TSA), Committee Neural Networks (CNN), Time domain simulation method, Artificial Neural Networks (ANN).