Localização de descargas parciais em transformadores de potência por meio de sistemas inteligentes e emissão acústica
Carregando...
Data
2017-05-31
Autores
Brunini, Danilo de Melo [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Estadual Paulista (Unesp)
Resumo
Os transformadores são equipamentos importantes do sistema elétrico de potência, possuem alto custo e suas falhas tem influência direta na qualidade da energia entregue aos consumidores. Uma das principais causas de falhas em transformadores imersos em líquido isolante, as descargas parciais, advém da degradação física e química do sistema de isolação devido à diversos fatores tais como sobrecarga, cargas não-lineares, chaveamento e superaquecimento. Essas descargas parciais aceleram a degradação do dielétrico do transformador e podem levar à destruição do equipamento, ocasionando elevado prejuízo financeiro. Dessa forma, são necessárias ações de prevenção de falhas causadas por descargas parciais em transformadores, através de métodos de monitoramento e localização. Este trabalho teve por objetivo apresentar um método de localização de descargas parciais em transformadores de potência imersos em óleo mineral isolante utilizando o método de emissão acústica e sistemas inteligentes do tipo redes neurais artificiais e algoritmos genéticos. Foram aplicadas métricas de processamento de sinais aos sinais acústicos gerados a partir de descargas parciais e obtidos através de sensores piezelétricos de baixo custo instalados no lado externo do tanque do transformador. Estas métricas foram utilizadas para treinamento das redes neurais a fim de obter a distância euclidiana entre os sensores e as descargas parciais. Essas distâncias euclidianas foram utilizadas em um sistema não-linear de localização o qual foi solucionado através de um algoritmo genético a fim de obter as coordenadas tridimensionais da descarga parcial. A análise dos resultados obtidos nas etapas de treinamento das redes neurais e execução do algoritmo genético foi realizada através de critérios estatísticos como erro absoluto médio, erro relativo percentual, desvio padrão e coeficiente de correlação. Esta técnica de localização mostrou resultados promissores uma vez que as coordenadas tridimensionais de duas descargas parciais, escolhidas para validação, apresentaram erros absolutos médios inferiores a 3 cm.
Transformers are important devices of the electric power system, which have high cost and their failures have a direct influence on the power quality delivered to the consumers. One of the main causes of failures in oil-immersed transformers, the partial discharges, comes from the physical and chemical degradation of the insulation system due to several factors such as overload, non-linear loads, switching and overheating. These partial discharges accelerate the degradation of the transformer dielectric and they can lead to the destruction of the equipment, causing high financial losses. Thus, actions are necessary to prevent faults caused by partial discharges in transformers, through monitoring and locating methods. The aim of this work was to present a method for locating partial discharges in oil-immersed power transformers using the acoustic emission method and intelligent systems such as artificial neural networks and genetic algorithms. Signal processing metrics were applied to the acoustic signals generated from partial discharges and obtained by low-cost piezoelectric sensors installed on the external side of the transformer tank. These metrics were used to train the neural networks in order to obtain the euclidean distance between the sensors and the partial discharges. These euclidean distances were used in a nonlinear location system, which was solved through a genetic algorithm in order to obtain the three-dimensional coordinates of the partial discharge. The analysis of the results obtained from the stages of neural networks training and genetic algorithm execution was performed through statistical criteria such as mean absolute error, percentage relative error, standard deviation and correlation coefficient. This localization technique showed promising results since the three-dimensional coordinates of two partial discharges, chosen for validation, presented mean absolute errors of less than 3 cm.
Transformers are important devices of the electric power system, which have high cost and their failures have a direct influence on the power quality delivered to the consumers. One of the main causes of failures in oil-immersed transformers, the partial discharges, comes from the physical and chemical degradation of the insulation system due to several factors such as overload, non-linear loads, switching and overheating. These partial discharges accelerate the degradation of the transformer dielectric and they can lead to the destruction of the equipment, causing high financial losses. Thus, actions are necessary to prevent faults caused by partial discharges in transformers, through monitoring and locating methods. The aim of this work was to present a method for locating partial discharges in oil-immersed power transformers using the acoustic emission method and intelligent systems such as artificial neural networks and genetic algorithms. Signal processing metrics were applied to the acoustic signals generated from partial discharges and obtained by low-cost piezoelectric sensors installed on the external side of the transformer tank. These metrics were used to train the neural networks in order to obtain the euclidean distance between the sensors and the partial discharges. These euclidean distances were used in a nonlinear location system, which was solved through a genetic algorithm in order to obtain the three-dimensional coordinates of the partial discharge. The analysis of the results obtained from the stages of neural networks training and genetic algorithm execution was performed through statistical criteria such as mean absolute error, percentage relative error, standard deviation and correlation coefficient. This localization technique showed promising results since the three-dimensional coordinates of two partial discharges, chosen for validation, presented mean absolute errors of less than 3 cm.
Descrição
Palavras-chave
Falhas em transformadores, Localização de descargas parciais, Emissão acústica, Sistemas inteligentes, Transformers faults, Location of partial discharges, Acoustic emission, Intelligent systems