activity
20182025
most citedEmbedding Power Flow into Machine Learning for Parameter and State Estimation

2 citations · 3 across the 2 of their papers we have counts for

collaborators

5 papers

eess.SY20212 cited

Embedding Power Flow into Machine Learning for Parameter and State Estimation

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 netw…

cs.LG20211 cited

Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems

Laurent Pagnier, Michael Chertkov

Parameter Estimation (PE) and State Estimation (SE) are the most wide-spread tasks in the system engineering. They need to be done automatically, fast and frequently, as measuremen…

eess.SY2020

Locating line and node disturbances in networks of diffusively coupled dynamical agents

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. Breakdown of such systems can have a dramatic impact, a…

math.OC2019

Optimal placement of inertia and primary control : a matrix perturbation theory approach

Laurent Pagnier, Philippe Jacquod

The increasing penetration of inertialess new renewable energy sources reduces the overall mechanical inertia available in power grids and accordingly raises a number of issues of…

nlin.AO2018

The Key Player Problem in Complex Oscillator Networks and Electric Power Grids: Resistance Centralities Identify Local Vulnerabilities

Melvyn Tyloo, Laurent Pagnier, Philippe Jacquod

Identifying key players in a set of coupled individual systems is a fundamental problem in network theory. Its origin can be traced back to social sciences and led to ranking algor…