Power System Model Identification via the Thermodynamic Formalism

This paper focuses upon the development of a methodology for data–driven construction of mesoscopic models of the T&D system for use in real-time monitoring and control. The system dynamics are lifted to a discrete covering space which provides an encoding of the system dynamics within symbol strings. These symbol strings are treated as Bernoulli shifts and are characterized, via the machinery of information theory and formal language theory, as probabilistic automata. As these automata are fundamentally pattern recognizers, they provide a fundamental basis for event/anomaly detection and thus a basis for critical grid monitoring functions such as security state identification.