Compresión y minería de datos para el diagnóstico de fallas incipientes de cortocircuito en transformadores monofásicos con carga eléctrica

Arantxa Contreras Valdes

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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