Solar cell panel crack detection using Particle Swarm Optimization algorithm

Amir Hossein Aghamohammadi, Anton Satria Prabuwono, Shahnorbanun Sahran, Marzieh Mogharrebi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

A solar cell panel as an efficient power source for the production of electrical energy has long been considered. Any defect on the solar cell panel's surface will be lead to reduced production of power and loss in the yield. In this case, inspection of the solar cell panel is essential to be performed to obtain a product of high quality. Some inspection methods have been developed, but in any event non-contact, non-destructive and efficient testing methods are necessary. This paper proposes an automated inspection system based on an image-processing approach for solar cell panel application in order to detect any cracks which may be appeared on the surface of solar cell panel. The Particle Swarm Optimization (PSO) algorithm as a main constituent of our proposed method is used for edge detection in the solar cell panel. Subsequently, some features like cracks and bus bars will be extracted and we will classify defected products and cracks based on the positions of the bus bars using Fuzzy logic. In this proposed method, an automated inspection system of solar cell panel proposed which has potential to get good results based on Particle Swarm optimization algorithm.

Original languageEnglish
Title of host publicationProceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011
Pages160-164
Number of pages5
Volume1
DOIs
Publication statusPublished - 2011
Event2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011 - Putrajaya
Duration: 28 Jun 201129 Jun 2011

Other

Other2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011
CityPutrajaya
Period28/6/1129/6/11

Fingerprint

Crack detection
Particle swarm optimization (PSO)
Solar cells
Inspection
Cracks
Edge detection
Fuzzy logic
Image processing
Defects
Testing

Keywords

  • automated inspection system
  • crack detection
  • PSO-based edge detection
  • Solar cell panel

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

Cite this

Aghamohammadi, A. H., Prabuwono, A. S., Sahran, S., & Mogharrebi, M. (2011). Solar cell panel crack detection using Particle Swarm Optimization algorithm. In Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011 (Vol. 1, pp. 160-164). [5976888] https://doi.org/10.1109/ICPAIR.2011.5976888

Solar cell panel crack detection using Particle Swarm Optimization algorithm. / Aghamohammadi, Amir Hossein; Prabuwono, Anton Satria; Sahran, Shahnorbanun; Mogharrebi, Marzieh.

Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011. Vol. 1 2011. p. 160-164 5976888.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Aghamohammadi, AH, Prabuwono, AS, Sahran, S & Mogharrebi, M 2011, Solar cell panel crack detection using Particle Swarm Optimization algorithm. in Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011. vol. 1, 5976888, pp. 160-164, 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011, Putrajaya, 28/6/11. https://doi.org/10.1109/ICPAIR.2011.5976888
Aghamohammadi AH, Prabuwono AS, Sahran S, Mogharrebi M. Solar cell panel crack detection using Particle Swarm Optimization algorithm. In Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011. Vol. 1. 2011. p. 160-164. 5976888 https://doi.org/10.1109/ICPAIR.2011.5976888
Aghamohammadi, Amir Hossein ; Prabuwono, Anton Satria ; Sahran, Shahnorbanun ; Mogharrebi, Marzieh. / Solar cell panel crack detection using Particle Swarm Optimization algorithm. Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011. Vol. 1 2011. pp. 160-164
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