Development of A Convolutional Neural Network Architecture for Production Based Photovoltaic Fault Detection

Muhammad Hussain, Tianhua Chen, Sofya Titarenko, Richard Hill, Hussain Al-Aqrabi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a framework for the detection of cracked Photovoltaic (PV) cells within the production environment through the creation of a Convolutional Neural Network (CNN). The paper demonstrates how a simple CNN using certain data augmentation techniques and specific regularization i.e., batch normalisation is efficient for PV based crack detection, achieving a recall rate of 99.2% and F1-score of 97.4%. We validate each methodological iteration via KFold cross validation providing granular metrical details allowing us to further optimize the model recall metric to suit the needs of our end deployment environment. We understand and appreciate the lack of highly quality PV image data of defect and therefore through our research we propose specific data augmentations for scaling and injecting variance into PV datasets. The augmentations do not provide any artificial propping of the model performance but are rather cases that may be found on the production line for PV cell manufacturing.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems
Subtitle of host publicationContributions Presented at the 21st UK Workshop on Computational Intelligence, September 7-9, 2022, Sheffield, UK
EditorsGeorge Panoutsos, Mahdi Mahfouf, Lyudmila S. Mihaylova
PublisherSpringer, Cham
Edition1st
ISBN (Electronic)9783031555688
ISBN (Print)9783031555671
Publication statusAccepted/In press - 20 Jul 2022
Event21st UK Workshop on Computational Intelligence - University of Sheffield, Sheffield, United Kingdom
Duration: 7 Sep 20229 Sep 2022
Conference number: 21
https://www.sheffield.ac.uk/ukci2022

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer Cham
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Workshop

Workshop21st UK Workshop on Computational Intelligence
Abbreviated titleUKCI 2022
Country/TerritoryUnited Kingdom
CitySheffield
Period7/09/229/09/22
Internet address

Fingerprint

Dive into the research topics of 'Development of A Convolutional Neural Network Architecture for Production Based Photovoltaic Fault Detection'. Together they form a unique fingerprint.

Cite this