公开(公告)号：US20240345142A1

公开(公告)日：2024-10-17

申请号：US18638531

申请日：2024-04-17

Applicant: Arizona Board of Regents on Behalf of Arizona State University

Inventor： Anamitra Pal ,  Behrouz Azimian ,  Lang Tong

IPC: G01R19/25

CPC classification number: G01R19/2513

Abstract: Time-synchronized state estimation for reconfigurable distribution systems is challenging because of limited real-time observability. A system addresses this challenge by formulating a deep learning (DL)-based approach for topology identification (TI) and unbalanced three-phase distribution system state estimation (DSSE). Two deep neural networks (DNNs) are trained for time-synchronized DNN-based TI and DSSE, respectively, for systems that are incompletely observed by synchrophasor measurement devices (SMDs) in real-time. A data-driven approach for judicious SMD placement to facilitate reliable TI and DSSE is also developed.