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 / 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

AUTOR(ES)
FONTE

IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia

DATA DE PUBLICAÇÃO

18/02/2011

RESUMO

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.

ASSUNTO(S)

processamento de alarmes sistema elétrico de potência diagnóstico de faltas redes neurais artificiais algoritmos genéticos engenharia eletrica electric power system alarm processing fault diagnosis artificial neural networks genetic algorithms

ACESSO AO ARTIGO

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