PowerFlowNet: Power flow approximation using message passing Graph Neural Networks-Reference-Cited by-同舟云学术

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PowerFlowNet: Power flow approximation using message passing Graph Neural Networks

Published:2024-09 Issue: Volume:160 Page:110112
ISSN:0142-0615
Container-title:International Journal of Electrical Power & Energy Systems
language:en
Short-container-title:International Journal of Electrical Power & Energy Systems

Author:

Lin Nan^ORCID,Orfanoudakis Stavros^ORCID,Cardenas Nathan Ordonez,Giraldo Juan S.^ORCID,Vergara Pedro P.^ORCID

Publisher

Elsevier BV

Reference32 articles.

1. Power flow analysis;Albadi,2019

2. Developments in the Newton Raphson power flow formulation based on current injections;da Costa;IEEE Trans Power Syst,1999

3. Load flow analysis by Gauss-Seidel method; a survey;Eltamaly;Int J Mechatron Electr Comput Technol (IJMEC),2017

4. GPU-based fast decoupled power flow with preconditioned iterative solver and inexact Newton method;Li;IEEE Trans Power Syst,2017

5. Electric power system planning: Issues, algorithms and solutions;Seifi,2011

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