Nonlinear loads determination using harmonic information in photovoltaic generation systems

Juan De DIos Fuentes; Alvaro D. Orjuela-Cañón; Héctor Iván Tangarife Escobar

doi:10.1109/ColCACI.2018.8484851

Nonlinear loads determination using harmonic information in photovoltaic generation systems

Juan De DIos Fuentes
, Alvaro D. Orjuela-Cañón
, Héctor Iván Tangarife Escobar

Producción científica: Redes de conocimiento › Actas de congreso › revisión exhaustiva

2 Citas (Scopus)

Resumen

This paper contains a proposal to determine the kind of nonlinear load when different appliances are connected to the solar generation system. A database built with sampled signals from the photovoltaic systems of the National Learning Service (SENA) in Bogota was employed. The methodology used information from harmonic distortion extracted from nonlinear loads, which was used as input in an artificial neural network with supervised learning. Two proposals were implemented. First one was based on energy information and second one was worked with wave peaks information. Results show that a classification rate of 95% could be reached in a problem with eight classes.

Idioma original	Inglés estadounidense
DOI	https://doi.org/10.1109/ColCACI.2018.8484851
Estado	Publicada - oct. 5 2018
Publicado de forma externa	Sí
Evento	1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Medellin, Colombia Duración: may. 16 2018 → may. 18 2018

Conferencia

Conferencia	1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018
País/Territorio	Colombia
Ciudad	Medellin
Período	5/16/18 → 5/18/18

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 7: Energía asequible y no contaminante

Áreas temáticas de ASJC Scopus

Inteligencia artificial
Informática aplicada

Acceder al documento

10.1109/ColCACI.2018.8484851

Otros archivos y enlaces

Citar esto

@conference{60020ce2f37847b2abf960c5c75c1b6e,

title = "Nonlinear loads determination using harmonic information in photovoltaic generation systems",

abstract = "This paper contains a proposal to determine the kind of nonlinear load when different appliances are connected to the solar generation system. A database built with sampled signals from the photovoltaic systems of the National Learning Service (SENA) in Bogota was employed. The methodology used information from harmonic distortion extracted from nonlinear loads, which was used as input in an artificial neural network with supervised learning. Two proposals were implemented. First one was based on energy information and second one was worked with wave peaks information. Results show that a classification rate of 95\% could be reached in a problem with eight classes.",

author = "\{De DIos Fuentes\}, Juan and Orjuela-Ca{\~n}{\'o}n, \{Alvaro D.\} and Escobar, \{H{\'e}ctor Iv{\'a}n Tangarife\}",

note = "Funding Information: El an{\'a}lisis realizado al sistema fotovoltaico del SENA es un pre{\'a}mbulo para trabajos complementarios relacionados con el desarrollo de interfaces o de dispositivos de supervisi{\'o}n y registro de la se{\~n}al el{\'e}ctrica, tambi{\'e}n podr{\'i}a ser de gran ayuda para el personal encargado de realizar an{\'a}lisis y conexiones de las diferentes cargas que est{\'a}n siendo conectadas al sistema. Funding Information: RECONOCIMIENTOS Los autores agradecen a la Universidad Antonio Nari{\~n}o que a trav{\'e}s de su proyecto “Dise{\~n}o e implementaci{\'o}n de un sistema inteligente de gesti{\'o}n de recursos para una microrred abastecida por energ{\'i}as alternativas” con c{\'o}digo 2017211, est{\'a} financiando el tiempo para este tipo de iniciativas. Los autores agradecen tambi{\'e}n, al Servicio Nacional de Aprendizaje SENA por facilitar las instalaciones del sistema fotovolt{\'a}ico para la investigaci{\'o}n realizada y al centro Metalmec{\'a}nico por permitir la participaci{\'o}n de instructores en el proyecto. Publisher Copyright: {\textcopyright} 2018 IEEE.; 1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 ; Conference date: 16-05-2018 Through 18-05-2018",

year = "2018",

month = oct,

day = "5",

doi = "10.1109/ColCACI.2018.8484851",

language = "English (US)",

}

Nonlinear loads determination using harmonic information in photovoltaic generation systems. / De DIos Fuentes, Juan; Orjuela-Cañón, Alvaro D.; Escobar, Héctor Iván Tangarife.
2018. Papel presentado en 1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018, Medellin, Colombia.

Producción científica: Redes de conocimiento › Actas de congreso › revisión exhaustiva

TY - CONF

T1 - Nonlinear loads determination using harmonic information in photovoltaic generation systems

AU - De DIos Fuentes, Juan

AU - Orjuela-Cañón, Alvaro D.

AU - Escobar, Héctor Iván Tangarife

N1 - Funding Information: El análisis realizado al sistema fotovoltaico del SENA es un preámbulo para trabajos complementarios relacionados con el desarrollo de interfaces o de dispositivos de supervisión y registro de la señal eléctrica, también podría ser de gran ayuda para el personal encargado de realizar análisis y conexiones de las diferentes cargas que están siendo conectadas al sistema. Funding Information: RECONOCIMIENTOS Los autores agradecen a la Universidad Antonio Nariño que a través de su proyecto “Diseño e implementación de un sistema inteligente de gestión de recursos para una microrred abastecida por energías alternativas” con código 2017211, está financiando el tiempo para este tipo de iniciativas. Los autores agradecen también, al Servicio Nacional de Aprendizaje SENA por facilitar las instalaciones del sistema fotovoltáico para la investigación realizada y al centro Metalmecánico por permitir la participación de instructores en el proyecto. Publisher Copyright: © 2018 IEEE.

PY - 2018/10/5

Y1 - 2018/10/5

N2 - This paper contains a proposal to determine the kind of nonlinear load when different appliances are connected to the solar generation system. A database built with sampled signals from the photovoltaic systems of the National Learning Service (SENA) in Bogota was employed. The methodology used information from harmonic distortion extracted from nonlinear loads, which was used as input in an artificial neural network with supervised learning. Two proposals were implemented. First one was based on energy information and second one was worked with wave peaks information. Results show that a classification rate of 95% could be reached in a problem with eight classes.

AB - This paper contains a proposal to determine the kind of nonlinear load when different appliances are connected to the solar generation system. A database built with sampled signals from the photovoltaic systems of the National Learning Service (SENA) in Bogota was employed. The methodology used information from harmonic distortion extracted from nonlinear loads, which was used as input in an artificial neural network with supervised learning. Two proposals were implemented. First one was based on energy information and second one was worked with wave peaks information. Results show that a classification rate of 95% could be reached in a problem with eight classes.

UR - https://www.scopus.com/pages/publications/85056445459

UR - https://www.scopus.com/pages/publications/85056445459#tab=citedBy

U2 - 10.1109/ColCACI.2018.8484851

DO - 10.1109/ColCACI.2018.8484851

M3 - Conference proceedings

AN - SCOPUS:85056445459

T2 - 1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018

Y2 - 16 May 2018 through 18 May 2018

ER -

Nonlinear loads determination using harmonic information in photovoltaic generation systems

Resumen

Conferencia

ODS de las Naciones Unidas

Áreas temáticas de ASJC Scopus

Acceder al documento

Otros archivos y enlaces

Huella

Citar esto