Scalability and Sample Efficiency Analysis of Graph Neural Networks for Power System State Estimation-Reference-Cited by-同舟云学术

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Scalability and Sample Efficiency Analysis of Graph Neural Networks for Power System State Estimation

Published:2023-06-05 Issue: Volume: Page:
ISSN:
Container-title:2023 International Balkan Conference on Communications and Networking (BalkanCom)
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Author:

Kundacina Ognjen¹,Gojic Gorana¹,Cosovic Mirsad²,Miskovic Dragisa¹,Vukobratovic Dejan³

Affiliation:

1. The Institute for Artificial Intelligence Research and Development of Serbia,Novi Sad,Serbia

2. University of Sarajevo,Faculty of Electrical Engineering,Sarajevo,Bosnia and Herzegovina

3. University of Novi Sad,Faculty of Technical Sciences,Novi Sad,Serbia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10167863/10167867/10167975.pdf?arnumber=10167975

Reference21 articles.

1. Electric power system state estimation

2. A Fast Decoupled State Estimator for Systems Measured by PMUs

3. State estimation and observability analysis for phasor measurement unit measured systems

4. Real-Time Power System State Estimation and Forecasting via Deep Unrolled Neural Networks

5. Data-Driven Learning-Based Optimization for Distribution System State Estimation

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