dc.contributor | Cardoso Junior, Ghendy | |
dc.contributor | http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4770600A7 | |
dc.contributor | Bezerra, Ubiratan Holanda | |
dc.contributor | http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4787768D6 | |
dc.contributor | Araújo, Olinto César Bassi de | |
dc.contributor | http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4768258E6 | |
dc.creator | Toller, Marcelo Brondani | |
dc.date.accessioned | 2017-05-30 | |
dc.date.accessioned | 2019-05-24T19:35:53Z | |
dc.date.available | 2017-05-30 | |
dc.date.available | 2019-05-24T19:35:53Z | |
dc.date.created | 2017-05-30 | |
dc.date.issued | 2011-02-18 | |
dc.identifier | TOLLER, Marcelo Brondani. Proposal of a hybrid system composed of artificial neural networks and genetic algorithms for the treatment of alarms, and fault diagnosis in electrical power system. 2011. 131 f. Dissertação (Mestrado em Engenharia Elétrica) - Universidade Federal de Santa Maria, Santa Maria, 2011. | |
dc.identifier | http://repositorio.ufsm.br/handle/1/8490 | |
dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/2835323 | |
dc.description.abstract | This work proposes a hybrid system for alarm processing and fault diagnosis in
electrical networks which use two methods of computational intelligence: Generalized
Regression Neural Network and Genetic Algorithms. The neural network has the function of
processing the set of received alarms and present as a response the characteristic(s) event(s),
using for this, an elaborated knowledge based on the functional diagrams for protection and
interviews with operators. Six modules were implemented for different neural components of
a test system, according to their protection schemes. The output of these modules is used as
input to the GA which has to do a combined analysis along with its database and provide the
operator with the main protective components involved in the incident, as well as the probable
causes of defects and actions to be taken in order to return the system in the shortest possible
time and greater safety. For average inserted random errors of 0%, 7,73%, 15,46% and
23,19% in the received alarms, the system was able to diagnoses correctly in 100%, 93,60%,
74,26% and 48,07% of the cases respectively. It was found that the genetic algorithm
improved the results obtained by neural network with good capability of generalization and
condition to present multiple solutions, and the response time of the hybrid system was
acceptable to the under consideration problem. | |
dc.publisher | Universidade Federal de Santa Maria | |
dc.publisher | BR | |
dc.publisher | Engenharia Elétrica | |
dc.publisher | UFSM | |
dc.publisher | Programa de Pós-Graduação em Engenharia Elétrica | |
dc.rights | Acesso Aberto | |
dc.subject | Sistema elétrico de potência | |
dc.subject | Processamento de alarmes | |
dc.subject | Diagnóstico de faltas | |
dc.subject | Redes neurais artificiais | |
dc.subject | Algoritmos genéticos | |
dc.subject | Electric power system | |
dc.subject | Alarm processing | |
dc.subject | Fault diagnosis | |
dc.subject | Artificial neural networks | |
dc.subject | Genetic algorithms | |
dc.title | Proposta de um sistema híbrido composto por redes neurais artificiais e algorítmos genéticos para o tratamento de alarmes e o diagnóstico de faltas em sistemas elétricos de potência | |
dc.type | Tesis | |