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Modeling of Harmonic Current in Electrical Grids with Photovoltaic Power Integration Using a Nonlinear Autoregressive with External Input Neural Networks

Author

Listed:
  • Adán Alberto Jumilla-Corral

    (Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali C.P. 21280, Baja California, Mexico)

  • Carlos Perez-Tello

    (Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali C.P. 21280, Baja California, Mexico)

  • Héctor Enrique Campbell-Ramírez

    (Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali C.P. 21280, Baja California, Mexico)

  • Zulma Yadira Medrano-Hurtado

    (Departamento de Ciencias Básicas, Instituto Tecnológico de Mexicali, Mexicali B.C. 21376, Baja California, Mexico)

  • Pedro Mayorga-Ortiz

    (Departamento de Eléctrica-Electrónica, Instituto Tecnológico de Mexicali, Mexicali B.C. 21376, Baja California, Mexico)

  • Roberto L. Avitia

    (Facultad de Ingeniería, Universidad Autónoma de Baja California, Mexicali C.P. 21280, Baja California, Mexico)

Abstract

This research presents the modeling and prediction of the harmonic behavior of current in an electric power supply grid with the integration of photovoltaic power by inverters using artificial neural networks to determine if the use of the proposed neural network is capable of capturing the harmonic behavior of the photovoltaic energy integrated into the user’s electrical grids. The methodology used was based on the use of recurrent artificial neural networks of the nonlinear autoregressive with external input type. Work data were obtained from experimental sources through the use of a test bench, measurement, acquisition, and monitoring equipment. The input–output parameters for the neural network were the current values in the inverter and the supply grid, respectively. The results showed that the neural network can capture the dynamics of the analyzed system. The generated model presented flexibility in data handling, allowing to represent and predict the behavior of the harmonic phenomenon. The obtained algorithm can be transferred to physical or virtual systems for the control or reduction of harmonic distortion.

Suggested Citation

  • Adán Alberto Jumilla-Corral & Carlos Perez-Tello & Héctor Enrique Campbell-Ramírez & Zulma Yadira Medrano-Hurtado & Pedro Mayorga-Ortiz & Roberto L. Avitia, 2021. "Modeling of Harmonic Current in Electrical Grids with Photovoltaic Power Integration Using a Nonlinear Autoregressive with External Input Neural Networks," Energies, MDPI, vol. 14(13), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:4015-:d:588017
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    Citations

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    Cited by:

    1. Luigi Fortuna & Arturo Buscarino, 2022. "Nonlinear Technologies in Advanced Power Systems: Analysis and Control," Energies, MDPI, vol. 15(14), pages 1-4, July.
    2. Dima Bykhovsky, 2022. "Experimental Lognormal Modeling of Harmonics Power of Switched-Mode Power Supplies," Energies, MDPI, vol. 15(2), pages 1-12, January.
    3. Min Yi & Wei Xie & Li Mo, 2021. "Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO," Energies, MDPI, vol. 14(20), pages 1-17, October.
    4. Gabriel Nicolae Popa, 2022. "Electric Power Quality through Analysis and Experiment," Energies, MDPI, vol. 15(21), pages 1-14, October.

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