• Samarth Patil
• Manish Jadhav
• Pranjal kharat
• Gayatri Chaudhari
• V.V. Jagtap

The Fastest-Growing Source Of Clean, Sustainable Energy, Solar Energy Outperforms All Other Types Of Energy Production. Solar Panels Typically Require Little Upkeep And Do Not Need Ongoing Maintenance. Numerous Issues, However, Might Cause A Production Loss Of Up To 20 Percent Because A Faulty Panel Can Affect The Generation Of The Entire Array. The Cost Of Repairs Will Be Lower If The Power Plant Is Properly Maintained On Time, But More Importantly, It Will Last Longer And Provide More Electricity Overall. Large Solar Plantations Require Expensive And Time-Consuming Manual Panel Monitoring, And It Is More Difficult For People To Get To Remote Solar Plantations. In This Article, Deep Learning-Based Methods For Detecting Faults In Photovoltaic Systems Are Presented Utilizing Thermal Images From An Unmanned Aerial Vehicle (UAV) Equipped With Infrared Sensors. The Software Which Will Be Produced As An Output Of This Research And Work Will Be Able To Detect Solar Panel Faults(Internal) Accurately And Will Be Able To Predict The Amount Of Solar Energy Produced As A Result Of These Defects Using The Voltage And Current Values Which Have Been Observed Previously. In Future Scope, This Software May Also Be Able Identify Faults From Images Of Solar Panels.

"Solar Panel Fault Detection System Using Deep Learning.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.a660-a668, November-2023, Available :http://www.jetir.org/papers/JETIR2311082.pdf

"Solar Panel Fault Detection System Using Deep Learning.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppa660-a668, November-2023, Available at : http://www.jetir.org/papers/JETIR2311082.pdf