  Due to the use of huge number of sensors and the increasing use of
communication networks, cyber-physical systems (CPS) are becoming vulnerable to
cyber-attacks. The ever-increasing complexity of CPS bring up the need for
data-driven machine learning applications to fill in the need of model creation
to describe the system behavior. In this paper, a novel stability condition
predictor based on cascaded feedforward neural network is proposed. The
proposed method aims to identify anomaly due to cyber or physical disturbances
as an early sign of instability. The proposed neural network utilizes cascaded
connections in order to increase accuracy of the prediction. The conjugate
gradient backpropagation and Polak-Ribi\`ere formula are utilized for training
process. This method also can predict the critical generators to mitigate the
effect of the cascading failure and consequent blackout in the system.
Simulations results on the IEEE 39-bus system indicate the superiority of the
proposed method in terms of accuracy, speed, and robustness.

Systems and Control;Systems and Control