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Model Reduction of Swing Equations with Physics Informed PDE
Such dynamics is normally modeled on seconds-to-tens-of-seconds time scales by the so-called swing equations, which are ordinary differential equations defined on a spatially discrete model of the power grid.
Embedding Power Flow into Machine Learning for Parameter and State Estimation
Modern state and parameter estimations in power systems consist of two stages: the outer problem of minimizing the mismatch between network observation and prediction over the network parameters, and the inner problem of predicting the system state for given values of the parameters.
Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems
Parameter Estimation (PE) and State Estimation (SE) are the most wide-spread tasks in the system engineering.
Locating line and node disturbances in networks of diffusively coupled dynamical agents
A wide variety of natural and human-made systems consist of a large set of dynamical units coupled into a complex structure.
