Situational awareness (SA) in simple terms is to understand the current state of the system and based on that understanding predict how system states are to evolve over time. Predictive modeling of power systems using conventional methods is time consuming and hence not well suited for real-time operation. In this study, neural network (NN) based non-linear predictor is used to predict states of power system for future time instance. Required control signals are computed based on predicted state variables and control set points. In order to reduce computation the problem is decoupled and solved in a cellular array of NNs. The cellular neural network (CNN) framework allows for accurate prediction with only minimal information exchange between neighboring predictors. The predicted states are then used in computing stability metrics that give proximity to point of instability. The situational awareness platform developed using CNN framework extracts information from data for the next time instance i.e. a step ahead of time and maps this data with geographical coordinates of power system components. The geographic information system (GIS) provides a visual indication of operating status of individual components as well as that of the entire system.
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