Automated Detection and Classification of Defects in Solar Photovoltaic Modules Using Mathematical Morphology Based on Area Criteria

DOI

10.2991/978-94-6463-926-1_23How to use a DOI?

Keywords

Classification; Defect; Mathematical Morphology; Photovoltaic

Abstract

The performance and reliability of solar photovoltaic (PV) systems are critically affected by defects such as cracks, chips, hotspots, and delamination, which can arise during manufacturing, transportation, or operation. Early detection and classification of these defects are essential to ensure optimal energy output and extend the lifespan of PV modules. This paper introduces an automated framework for detecting and classifying defects in solar PV modules using mathematical morphology (MM), with a specific focus on defect size as the primary classification criterion. By applying morphological operations such as erosion, dilation, opening, and closing, the proposed method isolates defect regions, computes their areas, and categorizes them into small, medium, and large classes based on predefined thresholds. The approach is computationally efficient, robust to noise, and validated on a synthetic dataset. Simulation results demonstrate high accuracy in defect detection and classification, supported by precision, recall, and F1-score to ensure balanced evaluation. Furthermore, this work discusses deployment challenges in real-world conditions, integration with existing PV monitoring systems, and operator training needs to enhance practical applicability.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - I Gusti Ngurah Agung Dwijaya Saputra
AU  - Wei Yao
AU  - I Ketut Suryawan
AU  - Ida Bagus Irawan Purnama
AU  - I Made Purbhawa
PY  - 2025
DA  - 2025/12/31
TI  - Automated Detection and Classification of Defects in Solar Photovoltaic Modules Using Mathematical Morphology Based on Area Criteria
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2025 (iCAST-ES 2025)
PB  - Atlantis Press
SP  - 194
EP  - 202
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-926-1_23
DO  - 10.2991/978-94-6463-926-1_23
ID  - Saputra2025
ER  -