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Discrimination of Nonlinear Loads in Electric Energy Generation Systems Using Harmonic Information

  • Juan de Dios Fuentes Velandia
  • Alvaro David Orjuela-CañónEmail author
  • Héctor Iván Tangarife Escobar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 833)

Abstract

This paper contains a proposal to determine the kind of nonlinear load when are connected to the solar or conventional generation system. A database was built with sampled signals extracted from the photovoltaic system of the National Learning Service (SENA) in Bogota, Colombia. The used methodology has an acquisition system of voltage signals, and then, information from harmonic distortion was employed to identify the nonlinear loads. An artificial neural network was implemented to discriminate appliances 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.

Keywords

Power quality Signal processing Neural networks Electric energy generation systems 

Notes

Acknowledgment

Authors want to thank to Universidad Antonio Nariño, that through the Project entitled “Design and implementation of an intelligent system for management of resources in a microgrid supplied by alternative energy” with project code 2017211 and publication code PI/UAN-2018-627GIBIO. In addition, the authors thank to National Learning System (SENA) for supporting this work through the PV system infrastructure used for this study and the metal mechanic center by the collaboration of the instructors involved in this project.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Servicio Nacional de AprendizajeBogotá D.C.Colombia
  2. 2.Universidad Antonio NariñoBogotá D.C.Colombia

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