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This work proposes a data-informed approach for an adaptable coordination ofdamping controllers. The novel concept of coordination is based on minimizingthe Total Action, a single metric that measures the system's dynamic responsepost-disturbance. This is a performance measure based on the physics of thepower system, which encapsulates the oscillation energy related to synchronousgenerators. Deep learning theory is used to propose a Total Action functionapproximator, which captures the relationship between the system wide-areameasurements, the status of damping controllers, and the conditions of thedisturbance. By commissioning the switching status (on/off) of dampingcontrollers in real-time, the oscillation energy is reduced, enhancing thepower system stability. The concept is tested in the Western North AmericaPower System (wNAPS) and compared with a model-based approach for thecoordination of damping controllers. The data-informed coordination outperformsthe model-based approach, demonstrating exceptional adaptability andperformance to handle multi-modal events. The proposed scheme shows outstandingreductions in low-frequency oscillations even under various operatingconditions, fault locations, and time delay considerations.

Coordination of Damping Controllers: A Data-Informed Approach for Adaptability