no code implementations • 12 Nov 2023 • Behrouz Azimian, Shiva Moshtagh, Anamitra Pal, Shanshan Ma
Recently, we demonstrated success of a time-synchronized state estimator using deep neural networks (DNNs) for real-time unobservable distribution systems.
no code implementations • 13 Dec 2022 • Shiva Moshtagh, Mehdi Rahmani
This paper introduces a two-level robust approach to estimate the unknown states of a large-scale power system while the measurements and network parameters are subjected to uncertainties.
no code implementations • 8 Dec 2022 • Shiva Moshtagh, Anwarul Islam Sifat, Behrouz Azimian, Anamitra Pal
Recently, there has been a major emphasis on developing data-driven approaches involving machine learning (ML) for high-speed static state estimation (SE) in power systems.
1 code implementation • 15 Apr 2021 • Behrouz Azimian, Reetam Sen Biswas, Shiva Moshtagh, Anamitra Pal, Lang Tong, Gautam Dasarathy
Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability.
