Substation apparatus failure plays a major role in reliability of power delivery systems. Traditionally, most utilities perform regular maintenance in order to prevent equipment breakdown. Condition-based maintenance strategy monitors the condition of the equipment by measuring and analyzing key parameters and recommends optimum maintenance actions. Equipment such as transformers and standby batteries which are valuable and critical assets in substations has attracted increased attentions in recently years. An automated monitoring and diagnosis tool for power transformers based on dissolved gas analysis, ANNEPS v4.0, was developed. The new tool extended the existing expert system and artificial neural network diagnostic engine with automated data acquisition, display, archiving, and alarm notification functions. This thesis also studied substation batteries types and failure mode and surveyed the market of current on-line battery monitors. A practical battery monitoring system architecture was proposed. Analysis rules of measured parameters were developed. The above study and results can provide basics for further designing of a simple battery monitoring system in industry applications. / Master of Science

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / O monitoramento e o diagnÃstico de falhas incipientes em transformadores de potÃncia imersos em Ãleo estÃo diretamente relacionados à avaliaÃÃo das condiÃÃes do sistema de isolamento. Neste estudo, estabelece-se o conceito de monitoramento e diagnÃstico, e em seguida tÃcnicas de monitoramento on-line sÃo discutidas. Um sistema de prÃ-diagnÃstico à elaborado baseado na utilizaÃÃo de um dispositivo on-line de monitoramento, Hydran da GE, para classificar a gravidade da situaÃÃo de falha detectada. Uma vez detectada uma situaÃÃo de falha, mÃdulos inteligentes de diagnÃstico de falhas incipientes, via redes neurais, podem ser utilizados para identificaÃÃo da falha interna do equipamento. Para completar a verificaÃÃo da qualidade dielÃtrica do lÃquido isolante, tambÃm à descrito um algoritmo inteligente, baseado em redes neurais, para diagnÃstico do estado do Ãleo atravÃs das grandezas fÃsico-quÃmicas. A relaÃÃo entre os atributos fÃsico-quÃmicos e as grandezas cromatogrÃficas referente ao Ãleo mineral tambÃm foram averiguadas. Foi desenvolvida, entÃo, a estimaÃÃo dos gases dissolvidos atravÃs das caracterÃsticas fÃsico-quÃmicas. Os mÃdulos de monitoramento on-line, diagnÃsticos do estado do Ãleo e de falhas incipientes, alÃm da estimaÃÃo dos gases dissolvidos, perfazem um sistema computacional de auxÃlio à operaÃÃo e manutenÃÃo. O sistema implementado apresenta resultados satisfatÃrios na implantaÃÃo em uma planta de usina termelÃtrica. / The monitoring and diagnosis of incipient fault in power transformers immerses in oil are directly related to the assessment of the isolation system conditions. In this research, it is established the concept of monitoring and diagnosis, after that, on-line monitoring techniques are discussed. A pre-diagnosis system is elaborated based on use of a monitoring on-line device, Hydran GE, to classify the situation gravity of the detected fault. Once detected a fault situation, intelligent modules of incipient fault diagnosis, by neural networks, can be used to identification of internal fault of the equipment. To complete the checking of the dielectric quality of the isolate liquid, it is also described an intelligent algorithm, based on neural networks, to diagnosis of the oil estate through physical-chemical attribute. The relation between physical-chemical attributes and chromatographic ones regarding to mineral oil were also verified. It was developed, then, the dissolved gases esteem through physicalchemical characteristics. The on-line monitoring modules, diagnosis of oil estate and incipient fault, besides dissolved gases esteem, constitute a computation aid system to operation and maintenance. The implemented system presents satisfied results in a thermoelectric power plant.

This dissertation is a systematic study of artificial intelligence (AI) applications for the diagnosis of power transformer incipient fault. The AI techniques include artificial neural networks (ANN, or briefly neural networks - NN), expert systems, fuzzy systems and multivariate regression. The fault diagnosis is based on dissolved gas-in-oil analysis (DGA). A literature review showed that the conventional fault diagnosis methods, i.e. the ratio methods (Rogers, Dornenburg and IEC) and the key gas method, have limitations such as the "no decision" problem. Various AI techniques may help solve the problems and present a better solution. Based on the IEC 599 standard and industrial experiences, a knowledge-based inference engine for fault detection was developed. Using historical transformer failure data from an industrial partner, a multi-layer perceptron (MLP) modular neural network was identified as the best choice among several neural network architectures. Subsequently, the concept of a hybrid diagnosis was proposed and implemented, resulting in a combined neural network and expert system tool (the ANNEPS system) for power transformer incipient diagnosis. The abnormal condition screening process, as well as the principle and algorithms of combining the outputs of knowledge based and neural network based diagnosis, were proposed and implemented in the ANNEPS. Methods of fuzzy logic based transformer oil/paper insulation condition assessment, and estimation of oil sampling interval and maintenance recommendations, were also proposed and implemented. Several methods of power transformer incipient fault location were investigated, and a 7Ã 21Ã 5 MLP network was identified as the best choice. Several methods for on-load tap changer (OLTC) coking diagnosis were also investigated, and a MLP based modular network was identified as the best choice. Logistic regression analysis was identified as a good auditor in neural network input pattern selection processes. The above results can help developing better power transformer maintenance strategies, and serve as the basis of on-line DGA transformer monitors. / Ph. D.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / O diagnÃstico de falhas incipientes em transformadores de potÃncia imersos em Ãleo està diretamente relacionado à avaliaÃÃo das condiÃÃes do sistema de isolamento. Este estudo aborda a relaÃÃo entre os gases dissolvidos no Ãleo e a qualidade do Ãleo mineral isolante utilizado em transformadores de potÃncia. As redes neurais artificiais sÃo utilizadas na abordagem da avaliaÃÃo das condiÃÃes operacionais do Ãleo isolante em transformadores de potÃncia, que à caracterizada por um comportamento dinÃmico nÃo-linear. As condiÃÃes de operaÃÃo e a integridade do sistema de isolamento de um transformador de potÃncia podem ser inferidas atravÃs das anÃlises fÃsico-quÃmicas e cromatogrÃficas (AnÃlise de GÃs Dissolvido). Estes ensaios permitem estabelecer procedimentos de operaÃÃo e manutenÃÃo do equipamento e normalmente sÃo realizados simultaneamente. Esta tese de doutorado propÃe um mÃtodo que pode ser usado para extrair informaÃÃes cromatogrÃficas usando as anÃlises fÃsico-quÃmicas atravÃs de redes neurais artificiais. As anÃlises atuais das propriedades fÃsico-quÃmicas fornecem apenas diagnÃstico do estado do Ãleo, o que nÃo permite o diagnÃstico de falhas incipientes. Acredita-se que, as concessionÃrias de energia podem melhorar a confiabilidade na previsÃo de falhas incipientes a um custo menor com este mÃtodo, uma vez que apenas um ensaio à necessÃrio. Os resultados mostraram que esta estratÃgia à promissora com mÃdia de acertos em diagnÃsticos de falhas maiores que 72%. O objetivo deste trabalho à a aplicaÃÃo direta do diagnÃstico de falhas incipientes atravÃs da utilizaÃÃo de propriedades fÃsico-quÃmicas, sem a necessidade de fazer uma cromatografia do Ãleo. / The diagnosis of incipient fault in power transformers immerses in oil are directly related to the assessment of the isolation system conditions. This search is about the relationship between dissolved gases and the quality of the insulating mineral oil used in power transformers. Artificial Neural Networks are used to approach operational conditions assessment issue of the insulating oil in power transformers, which is characterized by a nonlinear dynamic behavior. The operation conditions and integrity of a power transformer can be inferred by analysis of physicochemical and chromatographic (DGA â Dissolved Gas Analysis) profiles of the isolating oil. This tests allow establishing procedures for operating and maintaining the equipment and usually are performed simultaneously. This work proposes a method that can be used to extract chromatographic information using physicochemical analysis through Artificial Neural Networks. The present analysis of physicochemical properties only provide a diagnostic tool for the oil quality, which does not allow the diagnosis of incipient faults. ItÂs believed that, the power utilities could improve reliability in the prediction of incipient failures at a lower cost with this method, since only one test is required. The results show this strategy might be promising with an average accuracy for diagnosis of faults greater than 72%. The purpose of this work is the direct implementation of the diagnosis of incipient faults through the use of physicochemical properties without the need to make an oil chromatography.