Nonlinear loads determination using harmonic information in photovoltaic generation systems

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

Producción científica: Capítulo en Libro/ReporteContribución a la conferencia

1 Cita (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 originalInglés estadounidense
Título de la publicación alojada2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings
EditoresAlvaro David Orjuela-Canon, Diana Briceno Rodriguez
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781538667408
DOI
EstadoPublicada - oct. 5 2018
Publicado de forma externa
Evento1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Medellin, Colombia
Duración: may. 16 2018may. 18 2018

Serie de la publicación

Nombre2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings

Conferencia

Conferencia1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018
País/TerritorioColombia
CiudadMedellin
Período5/16/185/18/18

Áreas temáticas de ASJC Scopus

  • Inteligencia artificial
  • Informática aplicada

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