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The outage of a transmission line may change the system phase angledifferences to the point that the system experience stress conditions. Hence,the angle differences for post-contingency condition of a transmission linesshould be predicted in real time operation. However, online line-based phaseangle difference monitoring and prediction for power system stress assessmentis not a universal operating practice yet. Thus, in this paper, an online powersystem stress assessment framework is proposed by developing a convolutionalneural network (CNN) module trained through Deep Learning approach. In theproposed framework, the continuously streaming system phase angle data, drivenfrom phasor measurement units (PMUs) or a state estimator (SE), is used toconstruct power system stress indices adaptive to the structure parameters ofthe CNN module. Using this approach, any hidden patterns between phase anglesof buses and system stress conditions are revealed at low computation costwhile yielding accurate stress status and the severity of the stress. Theeffectiveness and scalability of the proposed method has been verified on theIEEE 118-bus and more importantly, on the PJM Interconnection system. Moreover,outperformance of the proposed method is verified by comparing the results withartificial neural network (ANN) and decision tree (DT).

An Online Deep Learning Approach Toward the Prediction of Power System Stresses Using Voltage Phasors