Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning-Reference-Cited by-同舟云学术

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Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning

Published:2019-11 Issue:6 Volume:34 Page:4910-4920
ISSN:0885-8950
Container-title:IEEE Transactions on Power Systems
language:
Short-container-title:IEEE Trans. Power Syst.

Author:

Mestav Kursat Rasim^ORCID,Luengo-Rozas Jaime,Tong Lang^ORCID

Funder

National Science Foundation

Power System Engineering Research Center

Fulbright Scholar Program, and Iberdrola Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Link

https://ieeexplore.ieee.org/ielam/59/8882519/8723114-aam.pdf

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1. Graph-learning-assisted state estimation using sparse heterogeneous measurements;Electric Power Systems Research;2024-10

2. A pseudo measurement modeling based forecasting aided state estimation framework for distribution network;International Journal of Electrical Power & Energy Systems;2024-09

3. Distribution System State Estimation Based on Power Flow-Guided GraphSAGE;Energies;2024-08-28

4. Normalizing Flows-based Probabilistic Learning-Aided Distribution System State Estimation;2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE);2024-08-06

5. Generative neural networks for providing pseudo-measurements in electric power distribution systems;Journal of the Brazilian Computer Society;2024-08-02

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