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dc.rights.license http://creativecommons.org/licenses/by-nc-nd/4.0 es_ES
dc.contributor Martin Valtierra Rodriguez es_ES
dc.creator Arantxa Contreras Valdes es_ES
dc.date 2020-09-28
dc.date.accessioned 2020-11-20T21:25:35Z
dc.date.available 2020-11-20T21:25:35Z
dc.date.issued 2020-09-28
dc.identifier.uri http://ri-ng.uaq.mx/handle/123456789/2349
dc.description Timely detection of short-circuit faults in transformers is an important challenge to ensure reliability and safety in the generation and transmission of electrical energy. With this detection, the reduction of costs and time required for device repair can be achieved, and at the same time be able to avoid severe damage to the equipment and large economic losses for both electricity providers and end users. Investigations related to the detection of electrical faults in transformers have shown that the most common fault that occurs in these is the short-circuit fault. The methodology developed in this research work allows the diagnosis of the incipient fault condition of a single phase transformer with electric load. In general, the methodology is based on the acquisition of multiple physical quantities such as temperature, current, voltage and vibrations. In this sense, it should be taken into account that the methodology presented proposes the implementation of a data compression algorithm based on a discrete wavelet transform to increase performance during data characterization and eliminate the noise contained in the signals. Then, the data is decompressed for analysis. The variance is extracted and subsequently the results are standardized to be analyzed by a data mining algorithm. A support vector machine is used as a classifier in order to detect the severity of the fault under different load conditions. The results obtained reflect the effectiveness and usefulness of the proposed methodology since it is possible to identify and classify the short-circuit faults induced in the transformer. es_ES
dc.format Adobe PDF es_ES
dc.language.iso Español es_ES
dc.relation.requires Si es_ES
dc.rights En Embargo es_ES
dc.subject Incipient fault detection es_ES
dc.subject Transformer es_ES
dc.subject Data compression es_ES
dc.subject Data mining es_ES
dc.subject Support vector machine es_ES
dc.subject.classification INGENIERÍA Y TECNOLOGÍA es_ES
dc.title Compresión y minería de datos para el diagnóstico de fallas incipientes de cortocircuito en transformadores monofásicos con carga eléctrica es_ES
dc.type Tesis de maestría es_ES
dc.creator.tid Clave CV CONACyT es_ES
dc.contributor.tid curp es_ES
dc.creator.identificador 892305 es_ES
dc.contributor.identificador VARM860915HGTLDR03 es_ES
dc.contributor.role Director es_ES
dc.degree.name Maestría en Ciencias (Mecatrónica) es_ES
dc.degree.department Facultad de Ingeniería es_ES
dc.degree.level Maestría es_ES


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