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The increasing coupling between the physical and communication layers in the cyber-physical system (CPS) brings up new challenges in system monitoring and control. Smart power grids with the integration of information and communication technologies are one of the most important types of CPS. Proper monitoring and control of the smart grid are highly dependent on the transient stability assessment (TSA). Effective TSA can provide system operators with insightful information on stability statuses and causes under various contingencies and cyber-attacks. In this study, a real-time stability condition predictor based on a feedforward neural network is proposed. The conjugate gradient backpropagation algorithm and Fletcher-Reeves updates are used for training, and the Kohonen learning algorithm is utilised to improve the learning process. By real-time assessment of the network features based on the minimum redundancy maximum relevancy algorithm, the proposed method can successfully predict transient stability and out of step conditions for the network and generators, respectively. Simulation results on the IEEE 39-bus test system indicate the superiority of the proposed method in terms of accuracy, precision, false positive rate, and true positive rate.

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-Ribière 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.

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