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Identification of PV Fault Classes Using Intelligent Method KNN (K-Nearest Neighbours)

Author

Listed:
  • Godfrey Benjamin Zulu

    (Mulungushi University, Department of electrical and electronics engineering)

  • Dr. C. Kara Mostefa Khelil

    (Université Djilali Bounaama Khemis Miliana)

  • Godfrey Murairidzi Gotora

    (Université Djilali Bounaama Khemis Miliana)

  • Taane Zahreddine

    (Arrupe Jesuit University, School of Engineering and ICT)

Abstract

Throughout many developing nations of our humble planet, renewable energy is a hot topic. Every country at this very moment is trying to move away from fossil fuels like petrol to complete renewable energy sources especially Photovoltaic systems. The reliability and efficiency of renewable energy systems is now a frequent topic of discussion. Like all systems of production, renewable energy systems are subject to failures and defects in their normal operating functions with regards to the amount of power output. These systems break down and deteriorate during the period of their operation. This is why a system of diagnostic is required whose many objectives is to provide indicators with the given valuables like temperature, solar irradiation, voltage and current output to detect the faults and thus maintain the energy production at optimum. The work in progress relates to the diagnostic of faults in the PV systems using artificial intelligent methods particularly the K-nearest Neighbour algorithm.

Suggested Citation

  • Godfrey Benjamin Zulu & Dr. C. Kara Mostefa Khelil & Godfrey Murairidzi Gotora & Taane Zahreddine, 2024. "Identification of PV Fault Classes Using Intelligent Method KNN (K-Nearest Neighbours)," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(8), pages 1202-1229, August.
    • Handle: RePEc:bjc:journl:v:11:y:2024:i:8:p:1202-1229
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