IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i5p790-d209448.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/5/790/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/5/790/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Manuela Panoiu & Caius Panoiu & Sergiu Mezinescu & Gabriel Militaru & Ioan Baciu, 2023. "Machine Learning Techniques Applied to the Harmonic Analysis of Railway Power Supply," Mathematics, MDPI, vol. 11(6), pages 1-20, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    2. Francisco G. Montoya & Raul Baños & Alfredo Alcayde & Maria G. Montoya & Francisco Manzano-Agugliaro, 2018. "Power Quality: Scientific Collaboration Networks and Research Trends," Energies, MDPI, vol. 11(8), pages 1-16, August.
    3. Eslami, Ahmadreza & Negnevitsky, Michael & Franklin, Evan & Lyden, Sarah, 2022. "Review of AI applications in harmonic analysis in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    4. Samet, Haidar, 2016. "Evaluation of digital metering methods used in protection and reactive power compensation of micro-grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 260-279.
    5. Coelho, Igor M. & Coelho, Vitor N. & Luz, Eduardo J. da S. & Ochi, Luiz S. & Guimarães, Frederico G. & Rios, Eyder, 2017. "A GPU deep learning metaheuristic based model for time series forecasting," Applied Energy, Elsevier, vol. 201(C), pages 412-418.
    6. Mahmud, Nasif & Zahedi, A., 2016. "Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 582-595.
    7. Gupta, Akhil, 2022. "Power quality evaluation of photovoltaic grid interfaced cascaded H-bridge nine-level multilevel inverter systems using D-STATCOM and UPQC," Energy, Elsevier, vol. 238(PB).
    8. Igual, R. & Medrano, C., 2020. "Research challenges in real-time classification of power quality disturbances applicable to microgrids: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    9. Paula Remigio-Carmona & Juan-José González-de-la-Rosa & Olivia Florencias-Oliveros & José-María Sierra-Fernández & Javier Fernández-Morales & Manuel-Jesús Espinosa-Gavira & Agustín Agüera-Pérez & José, 2022. "Current Status and Future Trends of Power Quality Analysis," Energies, MDPI, vol. 15(7), pages 1-18, March.
    10. Jannesar, Mohammad Rasol & Sedighi, Alireza & Savaghebi, Mehdi & Guerrero, Josep M., 2018. "Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration," Applied Energy, Elsevier, vol. 226(C), pages 957-966.
    11. Misael Lopez-Ramirez & Eduardo Cabal-Yepez & Luis M. Ledesma-Carrillo & Homero Miranda-Vidales & Carlos Rodriguez-Donate & Rocio A. Lizarraga-Morales, 2018. "FPGA-Based Online PQD Detection and Classification through DWT, Mathematical Morphology and SVD," Energies, MDPI, vol. 11(4), pages 1-15, March.
    12. Md Shafiullah & M. A. Abido & Taher Abdel-Fattah, 2018. "Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach," Energies, MDPI, vol. 11(9), pages 1-23, September.
    13. Vavilapalli, Sridhar & Umashankar, S. & Sanjeevikumar, P. & Ramachandaramurthy, Vigna K. & Mihet-Popa, Lucian & Fedák, Viliam, 2018. "Three-stage control architecture for cascaded H-Bridge inverters in large-scale PV systems – Real time simulation validation," Applied Energy, Elsevier, vol. 229(C), pages 1111-1127.
    14. Allard, Stéphane & Debusschere, Vincent & Mima, Silvana & Quoc, Tuan Tran & Hadjsaid, Nouredine & Criqui, Patrick, 2020. "Considering distribution grids and local flexibilities in the prospective development of the European power system by 2050," Applied Energy, Elsevier, vol. 270(C).
    15. Muttqi, Kashem M. & Aghaei, Jamshid & Askarpour, Mohammad & Ganapathy, Velappa, 2017. "Minimizing the steady-state impediments to solar photovoltaics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1329-1345.
    16. Jung, Jaesung & Onen, Ahmet & Russell, Kevin & Broadwater, Robert P., 2015. "Local steady-state and quasi steady-state impact studies of high photovoltaic generation penetration in power distribution circuits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 569-583.
    17. Yassine Amirat & Zakarya Oubrahim & Hafiz Ahmed & Mohamed Benbouzid & Tianzhen Wang, 2020. "Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter," Energies, MDPI, vol. 13(10), pages 1-15, May.
    18. Sridhar, V. & Umashankar, S., 2017. "A comprehensive review on CHB MLI based PV inverter and feasibility study of CHB MLI based PV-STATCOM," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 138-156.
    19. Jamal, Taskin & Urmee, Tania & Calais, Martina & Shafiullah, GM & Carter, Craig, 2017. "Technical challenges of PV deployment into remote Australian electricity networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1309-1325.
    20. Barote, L. & Marinescu, C., 2014. "Software method for harmonic content evaluation of grid connected converters from distributed power generation systems," Energy, Elsevier, vol. 66(C), pages 401-412.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:790-:d:209448. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.