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Data analysis methodology utilizing the statistical metrics weight of evidence (WoE) and information value (IV) to assist in asset management of power transformers

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dc.creator REMA, Gabriela Sampaio
dc.date.issued 2024-12-06
dc.identifier.uri https://repositorio.unifei.edu.br/jspui/handle/123456789/4218
dc.description.abstract The Brazilian electric power transmission sector faces a significant challenge involving the end of the regulatory useful life of several of equipment. Given the technical and economic infeasibility of renewing all depreciated assets from a regulatory standpoint, the need for an assertive risk management analysis and a reliable assessment of the physical useful life of assets is emphasized, especially power transformers, the main asset in the electrical energy transmission sector. In view of this scenario, the objective of the proposed thesis is to add value to asset management through the development of a data analysis methodology to assist in decision-making regarding the direction of maintenance investment in power transformers. Due to their similarity, reactors are also evaluated. To this end, data on moisture in the insulating oil of the equipment were used and the following categorical variables: voltage class, installation region (Regional), criticality, type, and age of the equipment. It is noteworthy that these variables are technical registry data of the assets, and the water content is an essential characteristic for determining the operational condition of the insulating oil, being one of the properties measured in the physicochemical tests. The original contribution of the thesis is the selection of categories with greater weight and categorical variables with higher predictive power using the statistical metrics Weight of Evidence (WoE) and Information Value (IV). Analyzing the predictive importance of a variable before developing a predictor can lead to better performing models. Furthermore, data based decisions lead to more assertive and proactive actions, and the prioritization of variables for evaluation is an important contribution, especially considering large equipment parks. The methodology was applied to a dataset of almost 10 thousand oil samples from 795 power transformers and reactors from the ISA CTEEP, electrical energy transmission company in Brazil, responsible for approximately 95% of the energy transmitted in the state of São Paulo and about 30% of all energy in Brazil. pt_BR
dc.language eng en
dc.publisher Universidade Federal de Itajubá pt_BR
dc.rights Acesso Aberto pt_BR
dc.subject Power transformers pt_BR
dc.subject Asset management pt_BR
dc.subject Preventive actions pt_BR
dc.subject Insulating oil pt_BR
dc.subject Moisture in the oil pt_BR
dc.subject Variables prioritization pt_BR
dc.subject Information value pt_BR
dc.subject Weight of evidence pt_BR
dc.title Data analysis methodology utilizing the statistical metrics weight of evidence (WoE) and information value (IV) to assist in asset management of power transformers pt_BR
dc.type Tese pt_BR
dc.date.available 2025-03-18
dc.date.available 2025-03-18T18:51:06Z
dc.date.accessioned 2025-03-18T18:51:06Z
dc.creator.Lattes http://lattes.cnpq.br/5492143777007871 pt_BR
dc.contributor.advisor1 BONATTO, Benedito Donizeti
dc.contributor.advisor1Lattes http://lattes.cnpq.br/8344250043719538 pt_BR
dc.contributor.advisor-co1 LIMA, Antonio Carlos Siqueira de
dc.contributor.advisor-co1Lattes http://lattes.cnpq.br/0342367279777983 pt_BR
dc.publisher.country Brasil pt_BR
dc.publisher.department IESTI - Instituto de Engenharia de Sistemas e Tecnologia da Informação pt_BR
dc.publisher.program Programa de Pós-Graduação: Doutorado - Engenharia Elétrica pt_BR
dc.publisher.initials UNIFEI pt_BR
dc.subject.cnpq CNPQ::ENGENHARIAS::ENGENHARIA ELÉTRICA::SISTEMAS ELÉTRICOS DE POTÊNCIA pt_BR
dc.relation.references REMA, Gabriela Sampaio. Data analysis methodology utilizing the statistical metrics weight of evidence (WoE) and information value (IV) to assist in asset management of power transformers. 2024. 83 f. Tese (Doutorado em Engenharia Elétrica) – Universidade Federal de Itajubá, Itajubá, 2024. pt_BR


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