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)


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.


Power quality Signal processing Neural networks Electric energy generation systems 



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.


  1. 1.
    Hakuta, K., Masaki, H., Nagura, M., Umeyama, N., Nagai, K.: Evaluation of various photovoltaic power generation systems. In: 2015 IEEE International Telecommunications Energy Conference (INTELEC), pp. 1–4 (2015)Google Scholar
  2. 2.
    Messenger, R.A., Abtahi, A.: Photovoltaic Systems Engineering. CRC Press, Boca Raton (2017)Google Scholar
  3. 3.
    Adekol, O.I., Almaktoof, A.M., Raji, A.K.: Design of a smart inverter system for Photovoltaic systems application. In: 2016 International Conference on the Industrial and Commercial Use of Energy (ICUE), pp. 310–317 (2016)Google Scholar
  4. 4.
    Vides-Prado, A., et al.: Techno-economic feasibility analysis of photovoltaic systems in remote areas for indigenous communities in the Colombian Guajira. Renew. Sustain. Energy Rev. 82, 4245–4255 (2018)CrossRefGoogle Scholar
  5. 5.
    Radomes Jr., A.A., Arango, S.: Renewable energy technology diffusion: an analysis of photovoltaic-system support schemes in Medellín. Colombia. J. Clean. Prod. 92, 152–161 (2015)CrossRefGoogle Scholar
  6. 6.
    Hernandez, J.A., Velasco, D., Trujillo, C.L.: Analysis of the effect of the implementation of photovoltaic systems like option of distributed generation in Colombia. Renew. Sustain. Energy Rev. 15, 2290–2298 (2011)CrossRefGoogle Scholar
  7. 7.
    Ortega, M.J., Hernández, J.C., García, O.G.: Measurement and assessment of power quality characteristics for photovoltaic systems: harmonics, flicker, unbalance, and slow voltage variations. Electr. Power Syst. Res. 96, 23–35 (2013)CrossRefGoogle Scholar
  8. 8.
    Benysek, G., Pasko, M.: Power Theories for Improved Power Quality. Springer, Heidelberg (2012). Scholar
  9. 9.
    Dugan, R.C., McGranaghan, M.F., Beaty, H.W.: Electrical Power Systems Quality. McGraw-Hill, New York (1996)Google Scholar
  10. 10.
    F II, I.: IEEE recommended practices and requirements for harmonic control in electrical power systems, New York, NY, USA (1993)Google Scholar
  11. 11.
    Gil Montoya, F., Manzano-Agugliaro, F., Gómez López, J., Sánchez Alguacil, P.: Power quality research techniques: Advantages and disadvantages. DYNA 79, 66–74 (2012)Google Scholar
  12. 12.
    Castañeda, A.M.B., Yanchenko, S., Meyer, J., Schegner, P.: Impact of supply voltage distortion on the harmonic emission of electronic household equipment. In: Simposio Internacional sobre la Calidad de la Energía Eléctrica-SICEL (2013)Google Scholar
  13. 13.
    Valtierra-Rodriguez, M., de Jesus Romero-Troncoso, R., Osornio-Rios, R.A., Garcia-Perez, A.: Detection and classification of single and combined power quality disturbances using neural networks. IEEE Trans. Ind. Electron. 61, 2473–2482 (2014)CrossRefGoogle Scholar
  14. 14.
    Raptis, T.E., Vokas, G.A., Langouranis, P.A., Kaminaris, S.D.: Total power quality index for electrical networks using neural networks. Energy Procedia 74, 1499–1507 (2015)CrossRefGoogle Scholar
  15. 15.
    Pedapenki, K.K., Gupta, S.P., Pathak, M.K.: Application of neural networks in power quality. In: 2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI), pp. 116–119 (2015)Google Scholar
  16. 16.
    Saini, M.K., Kapoor, R.: Classification of power quality events–a review. Int. J. Electr. Power Energy Syst. 43, 11–19 (2012)CrossRefGoogle Scholar
  17. 17.
    Khokhar, S., Zin, A.A.B.M., Mokhtar, A.S.B., Pesaran, M.: A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances. Renew. Sustain. Energy Rev. 51, 1650–1663 (2015)CrossRefGoogle Scholar
  18. 18.
    Barajas, M., Bañuelos-Sánchez, P.: Contaminación armónica producida por cargas no lineales de baja potencia: modelo matemático y casos prácticos. Ing. Investig. y Tecnol. 11 (2010)Google Scholar
  19. 19.
    Pinkus, A.: Approximation theory of the MLP model in neural networks. Acta Numer. 8, 143–195 (1999)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Haykin, S.: Neural Networks and Learning Machines. Prentice Hall, Upper Saddle River (2009)Google Scholar
  21. 21.
    Riedmiller, M., Rprop, I.: Rprop-description and implementation details (1994)Google Scholar
  22. 22.
    Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: 1993 IEEE International Conference on Neural Networks, pp. 586–591 (1993)Google Scholar
  23. 23.
    Kubat, M.: Neural Networks: A Comprehensive Foundation by Simon Haykin. Macmillan, New York, 1994 (1999). ISBN 0-02-352781-7Google Scholar

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© 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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