Data/hora: 05/06/2024 09:13:17

Provedor de dados: 	52	
País: 	Brazil	
Título: 	Power Flow Analysis and Self-recovery of Electrical Energy Distribution Network Using Artificial Neural Networks	
Autores: 	Avelar,Fabio da Silva; Fritzen,Paulo Cícero; Furucho,Mariana Antônia Aguiar.	
Data: 	2018-01-01	
Ano: 	2018	
Palavras-chave: 	Distribution Networks; Optimization; Self-recovery of networks; Smart Grid.	
Resumo: 	ABSTRACT A computational model for self-recovery of electricity distribution network was developed to simulate it, emulated by the IEEE 123 node model. The electrical system considered has automatic switches capable of identifying a momentary failure in the line and finding the best reconfiguration for its reclosing. An artificial neural network (ANN), backpropagation, was used to classify the type of failure and determine the best reconfiguration of the distribution network. Initially, five power failure scenarios were simulated in certain different parts of the power grid, and power flow analysis via OpenDSS was performed. Next, the most suitable switching was observed within the shortest time interval to restore the power supply. With the purpose of better visualization to identify the reclosing, an implementation was carried out via ELIPSE SCADA. In this way, it is possible to identify the faulted segment in order to isolate it, leaving the smallest number of consumers without power supply in shortest possible time. With the results of the simulations, tests and analyzes were performed to verify their robustness and speed, in the expectation that the model developed be faster than an experienced Operating Distribution Center.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132018000200223	
Editor: 	Instituto de Tecnologia do Paraná - Tecpar	
Relação: 	10.1590/1678-4324-smart-2018000320	
Formato: 	text/html	
Fonte: 	Brazilian Archives of Biology and Technology  v.61 n.spe 2018	
Fonte: 	info:eu-repo/semantics/openAccess