An effective imaging based diagnosis of Solar Photovoltaic System and performance parameters for smart solar power grid

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Published: Sep 3, 2024
DOI: https://doi.org/10.29070/ydpa5005
Keywords:
power generation, sources for residential, PV panels, machine learning, electricity output, large scale solar fields

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Authors

Kirti Pathak

Research Scholar, University of Technology, Jaipur, Rajasthan

Dr. Sanjay Kumar Jagannath Bagul

Supervisor, University of Technology, Jaipur, Rajasthan

Abstract

Another of the most attractive power generation sources for residential, corporate, and commercial operations is energy production. Due to this compelling qualities, photovoltaic systems (PV) electricity production have increasingly attracted the attention of researchers and professionals. Nonetheless, the greatest obstacle in generating energy from the sun is the predictable discontinuous output power from photovoltaic (PV) systems, which is largely caused by climate. The power differential of a photovoltaic panels may considerably reduce the financial profit of large scale solar fields. For everyday proper management of electricity supply production, distribution, and storing as well as for judgement on the electricity market, results were consistent prediction of the electricity output of solar PV systems is essential.


The 5 subjects mentioned in this article are:



  • The varied faults which might occur in PV panels; internet Photovoltaic panels monitoring;

  • Use of machine learning approaches in Photovoltaic module damage detection;

  • Advantages of fault diagnosis in PV panels and the various motion sensors used for this purpose are discussed.


Recommendation for future possible research paths are given in view of the evaluated research.

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Article Details

Issue

Vol. 18 No. 2 (2023): International Journal of Information Technology and Management (IJITM)

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Articles

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