Aplicación de la Metodología CRISP-DM en el Análisis de Gases Disueltos en Aceite Dieléctrico de Transformadores Eléctricos del Sector Eléctrico Ecuatoriano

Carlos Molina; Vladimir Bonilla

doi:10.37116/revistaenergia.v21.n1.2024.635

Application of CRISP-DM Methodology in the Analysis of Dissolved Gases in Dielectric oil of Electrical Transformers in the Ecuadorian Electrical Sector

Aplicación de la Metodología CRISP-DM en el Análisis de Gases Disueltos en Aceite Dieléctrico de Transformadores Eléctricos del Sector Eléctrico Ecuatoriano

Published 2024-07-24

https://doi.org/10.37116/revistaenergia.v21.n1.2024.635

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Vol. 21 No. 1 (2024): Revista Técnica "energía", Edición No. 21, ISSUE I

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SISTEMAS ELÉCTRICOS DE POTENCIA

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Application of CRISP-DM Methodology in the Analysis of Dissolved Gases in Dielectric oil of Electrical Transformers in the Ecuadorian Electrical Sector. (2024). Revista Técnica "energía", 21(1), PP. 12-21. https://doi.org/10.37116/revistaenergia.v21.n1.2024.635

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https://doi.org/10.37116/revistaenergia.v21.n1.2024.635

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Application of CRISP-DM Methodology in the Analysis of Dissolved Gases in Dielectric oil of Electrical Transformers in the Ecuadorian Electrical Sector. (2024). Revista Técnica "energía", 21(1), PP. 12-21. https://doi.org/10.37116/revistaenergia.v21.n1.2024.635

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Carlos Molina

Pontificia Universidad Católica

https://orcid.org/0009-0009-2655-0813

Vladimir Bonilla

Universidad Internacional del Ecuador

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https://orcid.org/0000-0001-6542-9666

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Machine Learning

Random Forest

CRISM-DM

Data Science

EDA

ETL

DGA

PCA

Overfitting

Transformer

This study addresses the application of the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology in the analysis of dissolved gases in oil of power transformers, being this a critical component in electrical systems. The adoption of this six-phase structured method allowed a comprehensive evaluation of the condition of the transformer units of the Ecuadorian electrical system based on the analysis of investment and expansion data of the sector, as well as the study of 1 099 DGA (Dissolved Gas Analysis) profiles obtained from a population of 153 transformers located in the different regions of continental Ecuador. The findings described in this work have the potential to significantly improve investment and maintenance strategies and policies. In addition, the adoption of automation techniques in the DGA classification process is proposed, using supervised learning models to enhance the reliability and efficiency of the public energy service. The results suggest that this approach not only improves the diagnosis within the maintenance activities, but also provides a solid basis to draw a roadmap towards a predictive asset management, resulting in a substantial improvement of the reliability of the national power system.

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