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

Research output: Knowledge networks › Conference proceedings › peer-review

2 Scopus citations

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.

Original language	English (US)
DOIs	https://doi.org/10.1109/ColCACI.2018.8484851
State	Published - Oct 5 2018
Externally published	Yes
Event	1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Medellin, Colombia Duration: May 16 2018 → May 18 2018

Conference

Conference	1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018
Country/Territory	Colombia
City	Medellin
Period	5/16/18 → 5/18/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications

Access to Document

10.1109/ColCACI.2018.8484851

Cite this

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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 {\textquotedblleft}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{\textquotedblright} 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",

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doi = "10.1109/ColCACI.2018.8484851",

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

Research output: Knowledge networks › Conference proceedings › peer-review

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.

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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

Abstract

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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