RT Journal Article
T1 A Deep Neural Network Approach for Online Topology Identification in State Estimation
A1 Gotti, Davide
A1 Amarís Duarte, Hortensia Elena
A1 Ledesma Larrea, Pablo
AB This paper introduces a network topology identification (TI) method based on deep neural networks (DNNs) for online applications. The proposed TI DNN utilizes the set of measurements used for state estimation to predict the actual network topology and offers low computational times along with high accuracy under a wide variety of testing scenarios. The training process of the TI DNN is duly discussed, and several deep learning heuristics that may be useful for similar implementations are provided. Simulations on the IEEE 14-bus and IEEE 39-bus test systems are reported to demonstrate the effectiveness and the small computational cost of the proposed methodology.
PB IEEE
SN 0885-8950
YR 2021
FD 2021-11-01
LK https://hdl.handle.net/10016/34917
UL https://hdl.handle.net/10016/34917
LA eng
NO This work was supported by the Spanish Ministry of Innovation under Grant PID2019-104449RB-I00. Paper no. TPWRS-01989-2020.
DS e-Archivo
RD 1 sept. 2024