Forced Oscillation Detection and Damping in Future Power Grids with High Penetration of Renewables

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Forced oscillation (FO) has recently been detected in actual power systems, i.e. Nordic and Western America power systems. These major events eventually result in the widespread blackout in the power system. Therefore, intensive research in the FO detection is sought. Numerous techniques have been successfully applied for the FO detection. Nevertheless, previous FO detection methods did not consider the impact of communication channels. To fill this gap, this work proposes a method to detect the FO taken into account impacts communication channels, which cooperates with artificial intelligent (AI) methods of ranking sources of the FO. The signal restoration technique will be applied to restore the quality of data so that the proposed technique can ensure small-signal and transient stabilities in large-scale power system. Previously, a small number of works focused on damping out the FO mode. The system may experience instability without proper FO detection and damping methods. For this reason, this work seeks a new technique for the FO detection and damping incorporating with AI approach in uncertain power systems with high penetrations of renewables, i.e. wind and solar generators. In this regard, impacts of uncertainties from renewables on the FO detection and damping will be analyzed. The power oscillation damper (POD) will be designed to simultaneously improve the damping of the FO mode and the inter-area mode. An adaptive control technique will be applied to enhance the FO mode along with moving window time without the installation of additional PODs. Besides, the event-triggered control strategy will be used to activate the functions of the new POD appropriately. By addressing the fundamental limitations in the FO detection and appropriate control methods, the definite recommendation will be made for the robust operation of the smart power grid with high penetration of renewables and various uncertainties.