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A continuous Online Monitoring System (OMS) for power system stability based on Phasor Measurements (PMU measurements) at all the generator buses is proposed in this paper. Unlike the state-of-the-art methods, the proposed OMS does not require information about fault clearance. This paper proposes a convolutional neural network, whose input is the heatmap representation of the measurements, for instability prediction. Through extensive simulations on standard IEEE 118-bus and IEEE 145-bus systems, the effectiveness of the proposed OMS is demonstrated under varying loading conditions, fault scenarios, topology changes, and generator parameter variations. Two different methods are also proposed to identify the set of critical generators that are most impacted in the unstable cases. [ABSTRACT FROM AUTHOR]

Interarea power oscillation damping enhancement in large interconnected power systems is often accomplished using supplementary controls at the synchronous machines involved and with the use of flexible ac transmission system devices. The power system literature has demonstrated that wide area signals could be more effective than the local signals for these controllers. In some cases where phasor measurement units are used, all the required control signals may not be available. To alleviate these cases, control signals are obtained using estimated signals from a “dynamic state estimator,” and a robust supplementary controller is deployed. In this paper, the cited strategy is illustrated using a static var compensator. The feasibility of the proposed method has been demonstrated on the IEEE 50-generator test system. [ABSTRACT FROM AUTHOR]