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Synchrophasor data suffer from quality issues like missing and bad data. Exploiting the low-rankness of the Hankel matrix of the synchrophasor data, this paper formulates the data recovery problem as a robust low-rank Hankel matrix completion problem and proposes a Bayesian data recovery method that estimates the posterior distribution of synchrophasor data from partial observations. In contrast to the deterministic approaches, our proposed Bayesian method provides an uncertainty index to evaluate the confidence of each estimation. To the best of our knowledge, this is the first method that provides confidence measure for synchrophasor data recovery. Numerical experiments on synthetic data and recorded synchrophasor data demonstrate that our method outperforms existing low-rank matrix completion methods. 

Forced oscillation events have become a challenging problem with the increasing penetration of renewable and other inverter-based resources (IBRs), especially when the forced oscillation frequency coincides with the dominant natural oscillation frequency. A severe forced oscillation event can deteriorate power system dynamic stability, damage equipment, and limit power transfer capability. This paper proposes a two-dimension scanning forced oscillation grid vulnerability analysis method to identify areas/zones in the system that are critical to forced oscillation. These critical areas/zones can be further considered as effective actuator locations for the deployment of forced oscillation damping controllers. Additionally, active power modulation control through IBRs is also proposed to reduce the forced oscillation impact on the entire grid. The proposed methods are demonstrated through a case study on a synthetic Texas power system model. The simulation results demonstrate that the critical areas/zones of forced oscillation are related to the areas that highly participate in the natural oscillations and the proposed oscillation damping controller through IBRs can effectively reduce the forced oscillation impact in the entire system.