Dissolved gas content forecasting in power transformers based on Least Square Support Vector Machine (LSSVM)
Título del documento: Dissolved gas content forecasting in power transformers based on Least Square Support Vector Machine (LSSVM)
Revista: Latin-American Journal of Computing (LAJC)
Base de datos:
Número de sistema: 000565095
ISSN: 1390-9134
Autores: 1
Instituciones: 1Escuela Politécnica Nacional,
Año:
Volumen: 4
Número: 3
Paginación: 55-60
País: Ecuador
Idioma: Inglés
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.
Keyword: Least Square Support Vector Machine (LSSVM).,
machine learning,
Gas chromatography,
Dissolved gas analysis (DGA)
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