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Harmonic Distortion Prediction Model of a Grid-Tie Photovoltaic Inverter Using an Artificial Neural Network

Author

Listed:
  • Matej Žnidarec

    (Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, J.J. Strossmayer University of Osijek, Osijek 31000, Croatia)

  • Zvonimir Klaić

    (Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, J.J. Strossmayer University of Osijek, Osijek 31000, Croatia)

  • Damir Šljivac

    (Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, J.J. Strossmayer University of Osijek, Osijek 31000, Croatia)

  • Boris Dumnić

    (Faculty of Technical Sciences, University of Novi Sad, Novi Sad 21000, Serbia)

Abstract

Expanding the number of photovoltaic (PV) systems integrated into a grid raises many concerns regarding protection, system safety, and power quality. In order to monitor the effects of the current harmonics generated by PV systems, this paper presents long-term current harmonic distortion prediction models. The proposed models use a multilayer perceptron neural network, a type of artificial neural network (ANN), with input parameters that are easy to measure in order to predict current harmonics. The models were trained with one-year worth of measurements of power quality at the point of common coupling of the PV system with the distribution network and the meteorological parameters measured at the test site. A total of six different models were developed, tested, and validated regarding a number of hidden layers and input parameters. The results show that the model with three input parameters and two hidden layers generates the best prediction performance.

Suggested Citation

  • Matej Žnidarec & Zvonimir Klaić & Damir Šljivac & Boris Dumnić, 2019. "Harmonic Distortion Prediction Model of a Grid-Tie Photovoltaic Inverter Using an Artificial Neural Network," Energies, MDPI, vol. 12(5), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:790-:d:209448
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    References listed on IDEAS

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    1. Kow, Ken Weng & Wong, Yee Wan & Rajkumar, Rajparthiban Kumar & Rajkumar, Rajprasad Kumar, 2016. "A review on performance of artificial intelligence and conventional method in mitigating PV grid-tied related power quality events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 334-346.
    2. Khokhar, Suhail & Mohd Zin, Abdullah Asuhaimi B. & Mokhtar, Ahmad Safawi B. & Pesaran, Mahmoud, 2015. "A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1650-1663.
    3. Fekete, Kresimir & Klaic, Zvonimir & Majdandzic, Ljubomir, 2012. "Expansion of the residential photovoltaic systems and its harmonic impact on the distribution grid," Renewable Energy, Elsevier, vol. 43(C), pages 140-148.
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