Clustering analysis for partial discharge detection in oil reactors

Ricardo Arias Velasquez; Felipa Elvira Munoz-Ccuro; Mercy Jeaninna Alvarado Mamani

doi:10.1109/EIRCON52903.2021.9613454

Clustering analysis for partial discharge detection in oil reactors

Ricardo Arias Velasquez
, Felipa Elvira Munoz-Ccuro
, Mercy Jeaninna Alvarado Mamani

Grupo de investigación en derecho penal

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

Reactors are the main equipment in the transmission systems, for voltage control in long overhead lines connected to the high voltage grid. The main problem is the one hundred percent of load or higher according the voltage level; usually in 500 kV grid, at the north of Peru, it is a nominal condition, therefore, the overload is a common practice. In this research article, it evaluates partial discharge with DGA analysis based on Support Vector Machine and clustering analysis associated to high voltage reactor. Suddenly increase of hydrogen; and its remaining life. Our findings in this paper is a new classification techniques for the reactors and a clustering analysis associated to partial discharge, it improves the accuracy compared with the power transformers. This paper is relevant due to knowledge engineering with the incorporation the new methodology for the partial discharge detection with high accuracy with 99.91% compared with transformer results of 97.55%.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781665444453
DOI	https://doi.org/10.1109/EIRCON52903.2021.9613454
Estado	Publicada - 1 ene. 2021
Evento	2nd IEEE Engineering International Research Conference, EIRCON 2021 - Virtual, Lima, Perú Duración: 27 oct. 2021 → 29 oct. 2021

Serie de la publicación

Nombre	Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021

Conferencia

Conferencia	2nd IEEE Engineering International Research Conference, EIRCON 2021
País/Territorio	Perú
Ciudad	Virtual, Lima
Período	27/10/21 → 29/10/21

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 7: Energía asequible y no contaminante

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10.1109/EIRCON52903.2021.9613454

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Velasquez, R. A., Munoz-Ccuro, F. E., & Mamani, M. J. A. (2021). Clustering analysis for partial discharge detection in oil reactors. En Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021 (Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EIRCON52903.2021.9613454

Velasquez, Ricardo Arias ; Munoz-Ccuro, Felipa Elvira ; Mamani, Mercy Jeaninna Alvarado. / Clustering analysis for partial discharge detection in oil reactors. Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021).

@inproceedings{b371ff55b6d749c499f01e44366a0446,

title = "Clustering analysis for partial discharge detection in oil reactors",

abstract = "Reactors are the main equipment in the transmission systems, for voltage control in long overhead lines connected to the high voltage grid. The main problem is the one hundred percent of load or higher according the voltage level; usually in 500 kV grid, at the north of Peru, it is a nominal condition, therefore, the overload is a common practice. In this research article, it evaluates partial discharge with DGA analysis based on Support Vector Machine and clustering analysis associated to high voltage reactor. Suddenly increase of hydrogen; and its remaining life. Our findings in this paper is a new classification techniques for the reactors and a clustering analysis associated to partial discharge, it improves the accuracy compared with the power transformers. This paper is relevant due to knowledge engineering with the incorporation the new methodology for the partial discharge detection with high accuracy with 99.91\% compared with transformer results of 97.55\%.",

keywords = "DGA, Degradation gas, Reactor, Remaining life, Root cause analysis",

author = "Velasquez, \{Ricardo Arias\} and Munoz-Ccuro, \{Felipa Elvira\} and Mamani, \{Mercy Jeaninna Alvarado\}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2nd IEEE Engineering International Research Conference, EIRCON 2021 ; Conference date: 27-10-2021 Through 29-10-2021",

year = "2021",

month = jan,

day = "1",

doi = "10.1109/EIRCON52903.2021.9613454",

language = "English",

series = "Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021",

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Velasquez, RA, Munoz-Ccuro, FE & Mamani, MJA 2021, Clustering analysis for partial discharge detection in oil reactors. En Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021. Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021, Institute of Electrical and Electronics Engineers Inc., 2nd IEEE Engineering International Research Conference, EIRCON 2021, Virtual, Lima, Perú, 27/10/21. https://doi.org/10.1109/EIRCON52903.2021.9613454

Clustering analysis for partial discharge detection in oil reactors. / Velasquez, Ricardo Arias; Munoz-Ccuro, Felipa Elvira; Mamani, Mercy Jeaninna Alvarado.
Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Clustering analysis for partial discharge detection in oil reactors

AU - Velasquez, Ricardo Arias

AU - Munoz-Ccuro, Felipa Elvira

AU - Mamani, Mercy Jeaninna Alvarado

PY - 2021/1/1

Y1 - 2021/1/1

N2 - Reactors are the main equipment in the transmission systems, for voltage control in long overhead lines connected to the high voltage grid. The main problem is the one hundred percent of load or higher according the voltage level; usually in 500 kV grid, at the north of Peru, it is a nominal condition, therefore, the overload is a common practice. In this research article, it evaluates partial discharge with DGA analysis based on Support Vector Machine and clustering analysis associated to high voltage reactor. Suddenly increase of hydrogen; and its remaining life. Our findings in this paper is a new classification techniques for the reactors and a clustering analysis associated to partial discharge, it improves the accuracy compared with the power transformers. This paper is relevant due to knowledge engineering with the incorporation the new methodology for the partial discharge detection with high accuracy with 99.91% compared with transformer results of 97.55%.

AB - Reactors are the main equipment in the transmission systems, for voltage control in long overhead lines connected to the high voltage grid. The main problem is the one hundred percent of load or higher according the voltage level; usually in 500 kV grid, at the north of Peru, it is a nominal condition, therefore, the overload is a common practice. In this research article, it evaluates partial discharge with DGA analysis based on Support Vector Machine and clustering analysis associated to high voltage reactor. Suddenly increase of hydrogen; and its remaining life. Our findings in this paper is a new classification techniques for the reactors and a clustering analysis associated to partial discharge, it improves the accuracy compared with the power transformers. This paper is relevant due to knowledge engineering with the incorporation the new methodology for the partial discharge detection with high accuracy with 99.91% compared with transformer results of 97.55%.

KW - DGA

KW - Degradation gas

KW - Reactor

KW - Remaining life

KW - Root cause analysis

UR - https://www.scopus.com/pages/publications/85123367087

U2 - 10.1109/EIRCON52903.2021.9613454

DO - 10.1109/EIRCON52903.2021.9613454

M3 - Conference contribution

AN - SCOPUS:85123367087

T3 - Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021

BT - Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE Engineering International Research Conference, EIRCON 2021

Y2 - 27 October 2021 through 29 October 2021

ER -

Velasquez RA, Munoz-Ccuro FE, Mamani MJA. Clustering analysis for partial discharge detection in oil reactors. En Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021). doi: 10.1109/EIRCON52903.2021.9613454

Clustering analysis for partial discharge detection in oil reactors

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