Data-driven adaptive dynamic coordination of damping controllers

This paper proposes a data-driven adaptive coordination of damping controllers to enhance power system stability. The coordination uses wide-area frequency measurements to select the switching status (on/off) of damping controllers (DC) enabled in electronically-interfaced resources (EIR). This is done by using the total action (TA), a dynamic performance measure of the oscillation energy related to the synchronous generators; and deep neural networks (DNNs), a powerful learning algorithm capable of providing accurate model regression between the grid measurements and the TA. The concept is tested in the Western North America Power System (wNAPS) and compared with a model-based approach for coordination of damping controllers. These are the first results of an extensive research related to coordination of DC-EIR, showing good adaptability and performance to different fault locations across the grid.