Please use this identifier to cite or link to this item: http://ri-ng.uaq.mx/handle/123456789/2349
Title: Compresión y minería de datos para el diagnóstico de fallas incipientes de cortocircuito en transformadores monofásicos con carga eléctrica
metadata.dc.creator: Arantxa Contreras Valdes
Keywords: INGENIERÍA Y TECNOLOGÍA;CIENCIAS TECNOLÓGICAS;INGENIERÍA Y TECNOLOGÍA ELÉCTRICAS
metadata.dc.date: 28-Sep-2020
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.
URI: http://ri-ng.uaq.mx/handle/123456789/2349
Other Identifiers: Incipient fault detection
Transformer
Data compression
Data mining
Support vector machine
Appears in Collections:Tesis

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