Interpretación de gases disueltos en aceite dieléctrico mediante redes neuronales para la detección de anomalías en transformadores de potencia de la subestación Novacero”.
The following document presents an automatic learning tool for the interpretation of dissolved gases in power transformers of the Novacero substation, using application algorithms such as neural networks and random forests with Python programming language. Through the results of gas chromatography t...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | Ecuador |
| Institución: | Universidad Técnica de Cotopaxi |
| Repositorio: | Repositorio Universidad Técnica de Cotopaxi |
| Idioma: | español |
| OAI Identifier: | oai:oai:repositorio.utc.edu.ec:27000:27000/10299 |
| Acceso en línea: | http://repositorio.utc.edu.ec/handle/27000/10299 |
| Access Level: | acceso abierto |
| Palabra clave: | ANÁLISIS DE GASES DISUELTOS TRANSFORMADORES DE POTENCIA REDES NEURONALES BOSQUES ALEATORIOS ELECTRICIDAD |
| Sumario: | The following document presents an automatic learning tool for the interpretation of dissolved gases in power transformers of the Novacero substation, using application algorithms such as neural networks and random forests with Python programming language. Through the results of gas chromatography tests in dielectric oil from several published articles, the data set delivered by the Analysis of Dissolved Gases (AGD) is used in quantities of parts per million (ppm), the amount of hydrocarbon gases as hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4) and acetylene (C2H2) that serve for learning and diagnosis of failure results. The algorithm implementation process is carried out with 128 training data and 64 test data to verify the proposed learning. |
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