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This thesis presents a unique design for an infrared spectrometer based on a MEMS Binary Fresnel Zone Plate for the purpose of assessing the health of oil-impregnated high voltage (HV) equipment. It does so by measuring dissolved gases within it. These gases include carbon monoxide, carbon dioxide, methane, ethane, ethylene, and acetylene. These gases are currently measured using numerous technologies such as gas combustion, gas chromatography, photoacoustic spectroscopy, and FTIR spectroscopy. Each of these technologies have their advantages and disadvantages. The design presented in this thesis consists of an analysis of how the various Binary Zone Plate parameters affect its spectral resolution and transmission efficiency. Simulations show that increasing the number of zones and the focal length, as well as decreasing the aperture diameter, increases the spectral resolution of the spectrometer. Simulations also show that transmission efficiency is proportional to the number of zones and the aperture diameter. This thesis presents a theoretical argument for how one zone plate lens can be used to measure all dissolved gases present in HV equipment. Lenses for the visible and infrared ranges were fabricated in the University of Manitoba NSFL Cleanroom. The lenses were then tested in an optical setup. The results show that the visible light experiments were successful in achieving appropriate spectral discrimination by changing the distance between the aperture and the lens. The results from the infrared experiment show that a detector was able to discriminate between full and no incident radiation. / May 2017

The power industry relies on high voltage transformers as the backbone of power distribution networks. High voltage transformers are designed to handle immense electrical loads in hostile environments. Long term placement is desired, however by being under constant heavy load transformers face mechanical, thermal, and electrical stresses which lead to failures of the protection systems in place. The service life of a transformer is often limited by the life time of its insulation system. Insulation failures most often develop from thermal faults, or hotspots, and electrical faults, or partial discharges. Detecting hotspots and partial discharges to predict transformer life times is imperative and much research is focused towards these topics. As these protection systems fail they often generate gas or acoustic signals signifying a problem. Research has already been performed discovering new ways integrate optical fiber sensors into high voltage transformers. This thesis is a continuation of that research by attempting to improve sensor sensitivity for hydrogen and acetylene gasses. Of note is the fabrication of new hydrogen sensing fiber for operation around a larger absorption peak and also the improvement of the acetylene sensor's light source stability. Also detailed is the manufacturing of a field testable prototype and the non-sensitivity testing of several other gasses. The developed sensors are capable but still could be improved with the use of more powerful and stable light sources. / Master of Science

This thesis presents an artificial neural network (ANN) approach to diagnose and detect faults in oil-filled power transformers based on dissolved gas-in-oil analysis. The goal of the research is to investigate the available transformer incipient fault diagnosis methods and then develop an ANN approach for this purpose. This ANN classifier should not only be able to detect the fault type, but also should be able to judge the cellulosic material breakdown. This classifier should also be able to accommodate more than one type of fault. This thesis describes a two-step ANN method that is used to detect faults with or without cellulose involved. Utilizing a feedforward artificial neural network, the classifier was trained with back-propagation, using training samples collected from different failed transformers. It is shown in the thesis that such a neural-net based approach can yield a high diagnosis accuracy. Several possible design alternatives and comparisons are also addressed in the thesis. The final system has been successfully tested, exhibiting a classification accuracy of 95% for major fault type and 90% for cellulose breakdown. / Master of Science

This dissertation thesis is focused on the evaluation of the dissolved gas analysis in the power oil transformer diagnosis. This thesis derives from known, in standards, directives and literature shows realities which are fill in new pieces of knowledge as determining of gases important for evaluation of the transformer condition, specifying of current methods accuracy and investigating of accuracy change for interpretation of dissolved gases. The part of this thesis is the application of mathematic methods for detailed description of individual transformer failures and determining of dominant gas which are generating in power oil transformer. Obtain conclusion will be contribution for transformer diagnostics and will be applicable in industrial practice.

Existem inúmeras ferramentas e metodologias para o diagnóstico de falhas em transformadores de potência, tanto para a monitoração e acompanhamento do equipamento em operação (planta fixa) como àquele retirado e abrigado em laboratórios de unidades fabris. Em função dos custos envolvidos no transporte dos equipamentos, manuseio para a substituição, energia não faturada nos períodos de manobras e custos adicionais dos ensaios para voltar com o equipamento em operação, as principais metodologias que se destacam são aquelas direcionadas ao equipamento em operação e também as escolhidas para serem analisadas neste trabalho. Após um estudo sistemático dessas metodologias, tais como termográfica, emissão acústica e análise dos gases dissolvidos no óleo, denominada cromatografia, verificou-se que esta última se destaca como a mais econômica e a mais difundida na identificação das falhas. Contudo, na cromatografia, existem diferentes critérios de análise baseados nas relações e quantidades de diferentes tipos de gases e que são usados pelas companhias concessionárias de forma indiscriminada, gerando muitas dúvidas quanto à sua validade. Esta pesquisa teve como principal foco a comparação desses critérios usando como base de dados àqueles fornecidos pela IEC e pela Companhia Paulista de Força e Luz (CPFL). A base de dados da CPFL contem aproximadamente quatro mil ensaios cromatográficos, colhidas nas ultimas três décadas correspondendo a 500 unidades transformadoras, nas potências de 5,0 a 50 MVA, instaladas em subestações nas tensões primárias de 69 e 138 kV, e secundária de 13,8 kV. Também fez parte dessa pesquisa a definição de um conjunto de critérios que melhor identificam as falhas em transformadores. / There are numerous tools and methodologies for fault diagnosis in power transformers, either for monitoring and tracking equipment in operation (fixed plant) or for those removed and housed in laboratories plants. Due to the costs involved in transporting the equipment, handling for the replacement, unbilled energy during periods of maneuvers and additional costs of the tests to return with the equipment in operation, the main methodologies that stand out are those using the equipment in operation and also those chosen to be analyzed in this work. After a systematic study of these methods such as thermography, and acoustic emission and analysis of gases dissolved in the oil, known as chromatography, it was found that the latter stands out as the most economical and most widely in the identification of faults. However, in chromatography, there are different criteria based on the relationships of different types and quantities of gases that are used by electrical companies indiscriminately, raising many questions about its validity. This research was mainly focused on the comparison of these criteria using the database as those provided by IEC and the Companhia Paulista de Força e Luz (CPFL). The database of CPFL contains approximately four thousand chromatographic assays from the last three decades, corresponding 500 transforming units, from 5 to 50 MVA, in substations with primary voltages of 138 kV and 69 and secondary of 13,8 kV. Was also studied the definition of a set of criteria which identify faults in transformers.

Existem inúmeras ferramentas e metodologias para o diagnóstico de falhas em transformadores de potência, tanto para a monitoração e acompanhamento do equipamento em operação (planta fixa) como àquele retirado e abrigado em laboratórios de unidades fabris. Em função dos custos envolvidos no transporte dos equipamentos, manuseio para a substituição, energia não faturada nos períodos de manobras e custos adicionais dos ensaios para voltar com o equipamento em operação, as principais metodologias que se destacam são aquelas direcionadas ao equipamento em operação e também as escolhidas para serem analisadas neste trabalho. Após um estudo sistemático dessas metodologias, tais como termográfica, emissão acústica e análise dos gases dissolvidos no óleo, denominada cromatografia, verificou-se que esta última se destaca como a mais econômica e a mais difundida na identificação das falhas. Contudo, na cromatografia, existem diferentes critérios de análise baseados nas relações e quantidades de diferentes tipos de gases e que são usados pelas companhias concessionárias de forma indiscriminada, gerando muitas dúvidas quanto à sua validade. Esta pesquisa teve como principal foco a comparação desses critérios usando como base de dados àqueles fornecidos pela IEC e pela Companhia Paulista de Força e Luz (CPFL). A base de dados da CPFL contem aproximadamente quatro mil ensaios cromatográficos, colhidas nas ultimas três décadas correspondendo a 500 unidades transformadoras, nas potências de 5,0 a 50 MVA, instaladas em subestações nas tensões primárias de 69 e 138 kV, e secundária de 13,8 kV. Também fez parte dessa pesquisa a definição de um conjunto de critérios que melhor identificam as falhas em transformadores. / There are numerous tools and methodologies for fault diagnosis in power transformers, either for monitoring and tracking equipment in operation (fixed plant) or for those removed and housed in laboratories plants. Due to the costs involved in transporting the equipment, handling for the replacement, unbilled energy during periods of maneuvers and additional costs of the tests to return with the equipment in operation, the main methodologies that stand out are those using the equipment in operation and also those chosen to be analyzed in this work. After a systematic study of these methods such as thermography, and acoustic emission and analysis of gases dissolved in the oil, known as chromatography, it was found that the latter stands out as the most economical and most widely in the identification of faults. However, in chromatography, there are different criteria based on the relationships of different types and quantities of gases that are used by electrical companies indiscriminately, raising many questions about its validity. This research was mainly focused on the comparison of these criteria using the database as those provided by IEC and the Companhia Paulista de Força e Luz (CPFL). The database of CPFL contains approximately four thousand chromatographic assays from the last three decades, corresponding 500 transforming units, from 5 to 50 MVA, in substations with primary voltages of 138 kV and 69 and secondary of 13,8 kV. Was also studied the definition of a set of criteria which identify faults in transformers.

Le travail de cette thèse avait pour objectif de contribuer à apporter de solutions au problème de surveillance des transformateurs par le biais des analyses de l'huile. La méthodologie et les résultats présentés s'appuient sur des cas concrets de transformateurs expertisés et des analyses d'huile réalisées en laboratoire. Les règles de diagnostic pour les grandeurs physico-chimiques ont été établies pour chaque équipement électrique et pour chaque type de fluide. Ces règles ont montré l'importance des paramètres liés à l'échantillonnage, à la méthode d'analyse et à la conception du transformateur et notamment l'importance du régleur en charge. Un lien entre analyse, défaut et origine du défaut a été établi et constitue la base de l'outil de diagnostic proposé. Ainsi l'outil identifie, à l'aide des analyses physico-chimiques réalisées sur le fluide, des défauts possibles et propose des origines de panne du transformateur. Finalement, une piste de pronostic d'état est abordée afin de développer le diagnostic. / The scope of this thesis was designed to contribute to solving the transformer monitoring problem of monitoring of transformers by analyses oil. The methodology and the results presented are based on concrete cases of assessed de-tanked transformers and oil analysis laboratory. The rules of diagnosis for physicochemical quantities have been established for each electrical equipment and for each type of fluid. These rules showed the importance of the sampling parameters, the method of analysis and the design of the transformer and especially the significance of the on load tap changer. A link between analysis, defect and origin of the defect was established which is the basis of the proposed diagnosis tool. Thanks to the chemical analyses, this tool enables to identify potential defects in the transformer and reveals some origins of failures.

M. Ing. / Fault detection tools have gained popularity in recent years due to the increasing need for reliable and predictable equipments. Transformer bushings account for the majority of transformer faults. Hence, to uphold the integrity of the power transmission and dis- tribution system, a tool to detect and identify faults in their developing stage is necessary in transformer bushings. Among the numerous tools for bushings monitoring, dissolved gas analysis (DGA) is the most commonly used. The advances in DGA and data storage capabilities have resulted in large amount of data and ultimately, the data analysis crisis. Consequent to that, computational intelligence methods have advanced to deal with this data analysis problem and help in the decision-making process. Numerous computational intelligence approaches have been proposed for bushing fault detection. Most of these approaches focus on the accuracy of prediction and not much research has been allocated to investigate the interpretability of the decisions derived from these systems. This work proposes a rough set theory (RST) model for bushing fault detection based on DGA data analyzed using the IEEEc57.104 and the IEC 60599 standards. RST is a rule-based technique suitable for analyzing vague, uncertain and imprecise data. RST extracts rules from the data to model the system. These rules are used for prediction and interpreting the decision process. The lesser the number of rules, the easier it is to interpret the model. The performance of the RST is dependent on the discretization technique employed. An equal frequency bin (EFB), Boolean reasoning (BR) and entropy partition (EP) are used to develop an RST model. The model trained using EFB data performs better than the models trained using BR and EP. The accuracy achieved is 96.4%, 96.0% and 91.3% for EFB, BR and EP respectively. This work also pro poses an ant colony optimization (ACO) for discretization. A model created using ACO discretized achieved an accuracy of 96.1%, which is compatible with the three methods above. When considering the overall performance, the ACO is a better discretization tool since it produces an accurate model with the least number of rules. The rough set tool proposed in this work is benchmarked against a multi-layer perceptron (MLP) and radial basis function (RBF) neural networks. Results prove that RST modeling for bushing is equally as capable as the MLP and better than RBF. The RST, MLP and RBF are used in an ensemble of classifiers. The ensemble performs better than the standalone models.

This dissertation thesis is focused on the evaluation of the dissolved gas analysis in the power oil transformer diagnosis. This thesis derives from known, in standards, directives and literature shows realities which are fill in new pieces of knowledge as determining of gases important for evaluation of the transformer condition, specifying of current methods accuracy and investigating of accuracy change for interpretation of dissolved gases. The part of this thesis is the application of mathematic methods for detailed description of individual transformer failures and determining of dominant gas which are generating in power oil transformer. Obtain conclusion will be contribution for transformer diagnostics and will be applicable in industrial practice.