Dissolved gas content forecasting in power transformers based on Least Square Support Vector Machine (LSSVM)

Dissolved gas content forecasting in power transformers based on Least Square Support Vector Machine (LSSVM)

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 correla...

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Bibliographic Details
Main Author:	Roberto Fiallos
Format:	Article
Language:	English
Published:	Escuela Politécnica Nacional (EPN) 2017-11-01
Series:	Latin-American Journal of Computing
Subjects:	dissolved gas analysis (dga) gas chromatography machine learning least square support vector machine (lssvm).
Online Access:	https://lajc.epn.edu.ec/index.php/LAJC/article/view/131

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