Search Results for author: Laurent Pagnier

Found 4 papers, 0 papers with code

Model Reduction of Swing Equations with Physics Informed PDE

no code implementations26 Oct 2021 Laurent Pagnier, Michael Chertkov, Julian Fritzsch, Philippe Jacquod

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.

Physics-informed machine learning

Embedding Power Flow into Machine Learning for Parameter and State Estimation

no code implementations26 Mar 2021 Laurent Pagnier, Michael Chertkov

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

no code implementations12 Feb 2021 Laurent Pagnier, Michael Chertkov

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

no code implementations17 Mar 2020 Robin Delabays, Laurent Pagnier, Melvyn Tyloo

A wide variety of natural and human-made systems consist of a large set of dynamical units coupled into a complex structure.

Time Series

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