| Revista: | Latin-American Journal of Computing (LAJC) |
| Base de datos: | |
| Número de sistema: | 000565095 |
| ISSN: | 1390-9134 |
| Autores: | Fiallos, Roberto J1 |
| Instituciones: | 1Escuela Politécnica Nacional, Quito, Pichincha. Ecuador |
| Año: | 2017 |
| Volumen: | 4 |
| Número: | 3 |
| Paginación: | 55-60 |
| País: | Ecuador |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Taking into account the chaotic characteristic of gas production within power transformers, a Least Square Support Vector Machine (LSSVM) model is implemented to forecast dissolved gas content based on historical chromatography samples. Additionally, an extending approach is developed with a correlation between oil temperature and Dissolved Gas Analysis (DGA), where a multi-input LSSVM is trained with the utilization of DGA and temperature datasets. The obtained DGA prediction from the extending model illustrates more accurate results, and the previous algorithm uncertainties are reduced.A favourable correlation between hydrogen, methane, ethane, ethylene, and acetylene and oil temperature is achieved by the application of the proposed multi-input model. |
| Disciplinas: | Ciencias de la computación, Ciencias de la computación |
| Palabras clave: | Procesamiento de datos, Inteligencia artificial |
| Keyword: | Data processing, Artificial intelligence |
| Texto completo: | Texto completo (Ver PDF) |
