Scholarlink

This research addresses critical aspects of solar photovoltaic (PV) system maintenance and monitoring to ensure sustained performance. Emphasizing solar panel reliability, the study employs image processing, clustering algorithms, and machine learning (K-Means, Naive Bayes) to detect and categorize factors impacting efficiency, such as dust accumulation and sunlight exposure. The developed system facilitates comprehensive assessment and classification, enhancing operational lifespan. Demonstrating versatility, the project incorporates alternative feature extraction and interactive threshold selection, ensuring adaptability to diverse scenarios. Experimental validation, including hotspot detection in thermal images, underscores the robustness of the proposed methodology, contributing significantly to solar panel monitoring and maintenance advancements.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine Learning and Thresholding Approach for Defects Classification in Solar Panels

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • G. H. Abhishek,
  • Anil Kumar,
  • Yashwant Kashyap

摘要

This research addresses critical aspects of solar photovoltaic (PV) system maintenance and monitoring to ensure sustained performance. Emphasizing solar panel reliability, the study employs image processing, clustering algorithms, and machine learning (K-Means, Naive Bayes) to detect and categorize factors impacting efficiency, such as dust accumulation and sunlight exposure. The developed system facilitates comprehensive assessment and classification, enhancing operational lifespan. Demonstrating versatility, the project incorporates alternative feature extraction and interactive threshold selection, ensuring adaptability to diverse scenarios. Experimental validation, including hotspot detection in thermal images, underscores the robustness of the proposed methodology, contributing significantly to solar panel monitoring and maintenance advancements.