RT Journal Article
T1 A fast data-driven topology identification method for dynamic state estimation applications
A1 Gotti, Davide
A1 Ledesma Larrea, Pablo
A1 Amarís Duarte, Hortensia Elena
AB This paper proposes a fast topology identification method to avoid estimation errors caused by network topology changes. The algorithm applies a deep neural network to determine the switching state of the branches that are relevant for the execution of a dynamic state estimator. The proposed technique only requires data from the phasor measurement units (PMUs) that are used by the dynamic state estimator. The proposed methodology is demonstrated working in conjunction with a frequency divider-based synchronous machine rotor speed estimator. A centralized and a decentralized approach are proposed using a modified version of the New England test system and the Institute of Electrical and Electronics Engineers (IEEE) 118-bus test system,respectively. The numerical results in both test systems show that the method demonstrate the reliability and the low computational burden of the proposed algorithm. The method achieves a satisfactory speed, the decentralized approach simplifies the training process and the algorithm proves to be robust in the face of wrong input data.
PB Elsevier
SN 0142-0615
YR 2023
FD 2023-05
LK https://hdl.handle.net/10016/36204
UL https://hdl.handle.net/10016/36204
LA eng
NO This work was funded by Agencia Estatal de Investigación MCIN/AEI/10.13039/501100011033 under Grant PID2019-104449RB-I00.
DS e-Archivo
RD 27 jul. 2024