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Solar Panel Dust Detection Classification and Efficiency Analysis Using Computer Vision and Deep Learning

  • Unique Paper ID: 168636
  • Volume: 11
  • Issue: 5
  • PageNo: 1780-1788
  • Abstract:
  • Solar panel efficiency is crucial for sustainable energy production, as any fault or obstruction in the panel can significantly reduce energy output. To ensure optimal performance and detect potential issues, real-time monitoring of solar panels is essential. Manual inspections are a common component of traditional procedures, but they take time and are prone to human mistake. Therefore, an automated system for detecting solar panel faults is necessary .This project proposes a machine learning-based solution for solar panel fault detection and classification using Convolution Neural Networks (CNN). The system consists of two models: one for detecting the presence of solar panels and another for classifying faults into categories such as "Bird-drop," "Dusty," "Electrical-damage," "Physical-Damage," and "Snow-Covered." The solution is designed to process real-time video streams from a camera, continuously updating the predictions on the user interface. Additionally, detected faults are kept in a database for further examination. Proposed model accurately identifies faults and enables proactive maintenance, thus improving the overall efficiency of solar energy systems. Through the combination of machine learning and image processing methods models, the system automates the fault detection process, reducing the need for manual inspection. The evaluation of the model on real-world data shows high accuracy in both detection and classification tasks, making it a reliable tool for solar panel monitoring.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 5
  • PageNo: 1780-1788

Solar Panel Dust Detection Classification and Efficiency Analysis Using Computer Vision and Deep Learning

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