23 citations · 76 across the 18 of their papers we have counts for
6 papers · 1 filter
Graph Neural Networks on Factor Graphs for Robust, Fast, and Scalable Linear State Estimation with PMUs
Ognjen Kundacina, Mirsad Cosovic, Dragisa Miskovic +1
As phasor measurement units (PMUs) become more widely used in transmission power systems, a fast state estimation (SE) algorithm that can take advantage of their high sample rates…
Scalability and Sample Efficiency Analysis of Graph Neural Networks for Power System State Estimation
Ognjen Kundacina, Gorana Gojic, Mirsad Cosovic +2
Data-driven state estimation (SE) is becoming increasingly important in modern power systems, as it allows for more efficient analysis of system behaviour using real-time measureme…
Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks
Ognjen Kundacina, Miodrag Forcan, Mirsad Cosovic +5
Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for…
Distributed Nonlinear State Estimation in Electric Power Systems using Graph Neural Networks
Ognjen Kundacina, Mirsad Cosovic, Dragisa Miskovic +1
Nonlinear state estimation (SE), with the goal of estimating complex bus voltages based on all types of measurements available in the power system, is usually solved using the iter…
Robust and Fast Data-Driven Power System State Estimator Using Graph Neural Networks
Ognjen Kundacina, Mirsad Cosovic, Dejan Vukobratovic
The power system state estimation (SE) algorithm estimates the complex bus voltages based on the available set of measurements. Because phasor measurement units (PMUs) are becoming…
State Estimation in Electric Power Systems Leveraging Graph Neural Networks
Ognjen Kundacina, Mirsad Cosovic, Dejan Vukobratovic
The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phaso…