This document outlines the advancements achieved in the development and implementation of an interface for automated defect detection in photovoltaic solar panels. The project, conducted at the Electricity, Electronics, and Telecommunications Center, aims to enhance the defect verification process o...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Online Access: |
https://revistas.sena.edu.co/index.php/CDITI/article/view/5750 |
| _version_ | 1862444310995140608 |
|---|---|
| author | Castillo-Méndez, Robinson |
| author_facet | Castillo-Méndez, Robinson |
| description | This document outlines the advancements achieved in the development and implementation of an interface for automated defect detection in photovoltaic solar panels. The project, conducted at the Electricity, Electronics, and Telecommunications Center, aims to enhance the defect verification process of solar panels undergoing the Electroluminescence (EL) test at the Solar Panel Testing Laboratory (LEPS). The text covers fundamental concepts and aspects of Machine Learning (ML), highlights key defects identifiable in EL images of solar panels, provides a high-level description of the proposed design solution, and presents significant validation results obtained from training and testing datasets. |
| format | Article |
| id | citeisa-5750 |
| institution | OJS - REV_TEINNOVA |
| record_format | dc |
| spellingShingle | Automatic classification for defects of photovoltaic solar cells using machine learning Castillo-Méndez, Robinson |
| title | Automatic classification for defects of photovoltaic solar cells using machine learning |
| url | https://revistas.sena.edu.co/index.php/CDITI/article/view/5750 |
