Power Flow Analysis and Self-recovery of Electrical Energy Distribution Network Using Artificial Neural Networks
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...
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| Format: | Article |
| Language: | English |
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Instituto de Tecnologia do Paraná (Tecpar)
2018-10-01
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| Series: | Brazilian Archives of Biology and Technology |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132018000200223&lng=en&tlng=en |
| _version_ | 1811309767040172032 |
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| author | Fabio da Silva Avelar Paulo Cícero Fritzen Mariana Antônia Aguiar Furucho |
| author_facet | Fabio da Silva Avelar Paulo Cícero Fritzen Mariana Antônia Aguiar Furucho |
| author_sort | Fabio da Silva Avelar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T09:46:54Z |
| format | Article |
| id | doaj.art-cddc11aeb0594b179f1ff0e18e4fcaa1 |
| institution | Directory Open Access Journal |
| issn | 1678-4324 |
| language | English |
| last_indexed | 2024-04-13T09:46:54Z |
| publishDate | 2018-10-01 |
| publisher | Instituto de Tecnologia do Paraná (Tecpar) |
| record_format | Article |
| series | Brazilian Archives of Biology and Technology |
| spelling | doaj.art-cddc11aeb0594b179f1ff0e18e4fcaa12022-12-22T02:51:43ZengInstituto de Tecnologia do Paraná (Tecpar)Brazilian Archives of Biology and Technology1678-43242018-10-0161spe10.1590/1678-4324-smart-2018000320S1516-89132018000200223Power Flow Analysis and Self-recovery of Electrical Energy Distribution Network Using Artificial Neural NetworksFabio da Silva AvelarPaulo Cícero FritzenMariana Antônia Aguiar FuruchoABSTRACT 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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132018000200223&lng=en&tlng=enDistribution NetworksOptimizationSelf-recovery of networksSmart Grid |
| spellingShingle | Fabio da Silva Avelar Paulo Cícero Fritzen Mariana Antônia Aguiar Furucho Power Flow Analysis and Self-recovery of Electrical Energy Distribution Network Using Artificial Neural Networks Brazilian Archives of Biology and Technology Distribution Networks Optimization Self-recovery of networks Smart Grid |
| title | Power Flow Analysis and Self-recovery of Electrical Energy Distribution Network Using Artificial Neural Networks |
| title_full | Power Flow Analysis and Self-recovery of Electrical Energy Distribution Network Using Artificial Neural Networks |
| title_fullStr | Power Flow Analysis and Self-recovery of Electrical Energy Distribution Network Using Artificial Neural Networks |
| title_full_unstemmed | Power Flow Analysis and Self-recovery of Electrical Energy Distribution Network Using Artificial Neural Networks |
| title_short | Power Flow Analysis and Self-recovery of Electrical Energy Distribution Network Using Artificial Neural Networks |
| title_sort | power flow analysis and self recovery of electrical energy distribution network using artificial neural networks |
| topic | Distribution Networks Optimization Self-recovery of networks Smart Grid |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132018000200223&lng=en&tlng=en |
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