Our team specializes in power system dynamics and state estimation. We exploit notions from control theory, dynamical systems, robust statistics, and signal processing.

Electric power systems undergo profound and radical changes triggered by new technologies in generation and storage, sensing and communications, optimization and control, power electronics, machine learning, and data science. These developments center around the need for integrating at a significant scale renewable energy sources, breaking through the electric power generation paradigm, and transforming how nations build their energy infrastructure. Of particular interest to our group, the dynamics of converters, driven by controls, are substantially different from that of legacy synchronously rotating machinery.

For instance, on a timescale, the dynamics of converters partially overlap with that of the synchronous generators' controllers, enabling their potentially adverse interaction. These facts accentuate the need for real-time, coordinated autonomous control schemes with humans out-of-the-loop and motivate us to pursue applications of the Koopman operator theory. Given the continuous deployment of advanced sensing and measurement systems, it is in this realm that a data-centric architecture is exciting and might offer attractive solutions.