9 citations · 14 across the 6 of their papers we have counts for
6 papers
Machine Learning-Based State Estimation for an Actual Transmission System Using Field PMU Data
Shiva Moshtagh, Nihar Thakkar, Anamitra Pal +1
Time-synchronized state estimation (SE) plays a critical role in ensuring real-time situational awareness in modern power systems. However, achieving full system observability usin…
Topology-Aware Graph Neural Network-based State Estimation for PMU-Unobservable Power Systems
Shiva Moshtagh, Behrouz Azimian, Mohammad Golgol +1
Traditional optimization-based techniques for time-synchronized state estimation (SE) often suffer from high online computational burden, limited phasor measurement unit (PMU) cove…
Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation
Behrouz Azimian, Shiva Moshtagh, Anamitra Pal +1
Recently, we demonstrated success of a time-synchronized state estimator using deep neural networks (DNNs) for real-time unobservable distribution systems. In this letter, we provi…
Two-level Robust State Estimation for Multi-Area Power Systems Under Bounded Uncertainties
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 uncert…
Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes
Shiva Moshtagh, Anwarul Islam Sifat, Behrouz Azimian +1
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. The emp…
State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks
Behrouz Azimian, Reetam Sen Biswas, Shiva Moshtagh +3
Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability. This paper addresses this challenge by formul…