Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning

[Display omitted] •State-of-the-art framework is proposed for automatic defect detection in PV modules.•Infrared images dataset of normal operating and defective PV modules is collected.•Isolated and develop-model transfer deep learning frameworks are proposed.•Isolated & transfer learned method...

Full description

Saved in:

Bibliographic Details
Published in:	Solar energy Vol. 198; pp. 175 - 186
Main Authors:	Akram, M. Waqar, Li, Guiqiang, Jin, Yi, Chen, Xiao, Zhu, Changan, Ahmad, Ashfaq
Format:	Journal Article
Language:	English
Published:	New York Elsevier Ltd 01.03.2020 Pergamon Press Inc
Subjects:	Accuracy Artificial neural networks Automatic defect detection Automation Datasets Deep learning Defects Develop-model transfer deep learning Electroluminescence Infrared imagery Infrared images Infrared imaging Inspection Isolated deep learning Labeling Machine learning Modules Monitoring methods Neural networks Performance enhancement Photovoltaic (PV) modules Photovoltaic cells Photovoltaics Solar energy Thermography Transfer learning Isolated deep learning Automatic defect detection Develop-model transfer deep learning Photovoltaic (PV) modules Infrared images Thermography
ISSN:	0038-092X, 1471-1257
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	[Display omitted] •State-of-the-art framework is proposed for automatic defect detection in PV modules.•Infrared images dataset of normal operating and defective PV modules is collected.•Isolated and develop-model transfer deep learning frameworks are proposed.•Isolated & transfer learned methods give 98.67% and 99.23% accuracy respectively.•These frameworks are qualitatively evaluated with experimental testing. With the rising use of photovoltaic and ongoing installation of large-scale photovoltaic systems worldwide, the automation of photovoltaic monitoring methods becomes important, as manual/visual inspection has limited applications. This research work deals with automatic detection of photovoltaic module defects in Infrared images with isolated deep learning and develop-model transfer deep learning techniques. An Infrared images dataset containing infrared images of normal operating and defective modules is collected and used to train the networks. The dataset is obtained from Infrared imaging performed on normal operating and defective photovoltaic modules with lab induced defects. An isolated learned model is trained from scratch using a light convolutional neural network design that achieved an average accuracy of 98.67%. For transfer learning, a base model is first developed (pre-trained) from electroluminescence images dataset of photovoltaic cells and then fine-tuned on infrared images dataset, that achieved an average accuracy of 99.23%. Both frameworks require low computation power and less time; and can be implemented with ordinary hardware. They also maintained real time prediction speed. The comparison shows that the develop-model transfer learning technique can help to improve the performance. In addition, we reviewed different kind of defects detectable from infrared imaging of photovoltaic modules, that can help in manual labelling for identifying different defect categories upon access to new huge data in future studies. Last of all, the presented frameworks are applied for experimental testing and qualitative evaluation.
AbstractList	With the rising use of photovoltaic and ongoing installation of large-scale photovoltaic systems worldwide, the automation of photovoltaic monitoring methods becomes important, as manual/visual inspection has limited applications. This research work deals with automatic detection of photovoltaic module defects in Infrared images with isolated deep learning and develop-model transfer deep learning techniques. An Infrared images dataset containing infrared images of normal operating and defective modules is collected and used to train the networks. The dataset is obtained from Infrared imaging performed on normal operating and defective photovoltaic modules with lab induced defects. An isolated learned model is trained from scratch using a light convolutional neural network design that achieved an average accuracy of 98.67%. For transfer learning, a base model is first developed (pre-trained) from electroluminescence images dataset of photovoltaic cells and then fine-tuned on infrared images dataset, that achieved an average accuracy of 99.23%. Both frameworks require low computation power and less time; and can be implemented with ordinary hardware. They also maintained real time prediction speed. The comparison shows that the develop-model transfer learning technique can help to improve the performance. In addition, we reviewed different kind of defects detectable from infrared imaging of photovoltaic modules, that can help in manual labelling for identifying different defect categories upon access to new huge data in future studies. Last of all, the presented frameworks are applied for experimental testing and qualitative evaluation. [Display omitted] •State-of-the-art framework is proposed for automatic defect detection in PV modules.•Infrared images dataset of normal operating and defective PV modules is collected.•Isolated and develop-model transfer deep learning frameworks are proposed.•Isolated & transfer learned methods give 98.67% and 99.23% accuracy respectively.•These frameworks are qualitatively evaluated with experimental testing. With the rising use of photovoltaic and ongoing installation of large-scale photovoltaic systems worldwide, the automation of photovoltaic monitoring methods becomes important, as manual/visual inspection has limited applications. This research work deals with automatic detection of photovoltaic module defects in Infrared images with isolated deep learning and develop-model transfer deep learning techniques. An Infrared images dataset containing infrared images of normal operating and defective modules is collected and used to train the networks. The dataset is obtained from Infrared imaging performed on normal operating and defective photovoltaic modules with lab induced defects. An isolated learned model is trained from scratch using a light convolutional neural network design that achieved an average accuracy of 98.67%. For transfer learning, a base model is first developed (pre-trained) from electroluminescence images dataset of photovoltaic cells and then fine-tuned on infrared images dataset, that achieved an average accuracy of 99.23%. Both frameworks require low computation power and less time; and can be implemented with ordinary hardware. They also maintained real time prediction speed. The comparison shows that the develop-model transfer learning technique can help to improve the performance. In addition, we reviewed different kind of defects detectable from infrared imaging of photovoltaic modules, that can help in manual labelling for identifying different defect categories upon access to new huge data in future studies. Last of all, the presented frameworks are applied for experimental testing and qualitative evaluation.
Author	Li, Guiqiang Akram, M. Waqar Chen, Xiao Zhu, Changan Ahmad, Ashfaq Jin, Yi
Author_xml	– sequence: 1 givenname: M. Waqar surname: Akram fullname: Akram, M. Waqar organization: Department of Precision Machinery and Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, China – sequence: 2 givenname: Guiqiang surname: Li fullname: Li, Guiqiang email: guiqiang.li@hull.ac.uk organization: School of Engineering, University of Hull, Hull HU6 7RX, UK – sequence: 3 givenname: Yi surname: Jin fullname: Jin, Yi email: jinyi08@ustc.edu.cn organization: Department of Precision Machinery and Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, China – sequence: 4 givenname: Xiao surname: Chen fullname: Chen, Xiao organization: State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei, Anhui 230026, China – sequence: 5 givenname: Changan surname: Zhu fullname: Zhu, Changan organization: Department of Precision Machinery and Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, China – sequence: 6 givenname: Ashfaq orcidid: 0000-0001-5559-043X surname: Ahmad fullname: Ahmad, Ashfaq organization: Department of Precision Machinery and Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, China
BookMark	eNqFkEFrXCEQx6Wk0M22H6Eg9Pxe1edbXXooISRtIJBLAr2Jq2Pi8lZf1d0QyIfvLJtTLwFBmPn_Rud3Ts5STkDIV856zvjq-7aveYIEpRdMsJ7xno3jB7LgUvGOi1GdkQVjg-7YWvz5RM5r3TLGFddqQV4v9i3vbIuOemjgWsyJ5kDnp9zyIU_NYmeX_X4CDAQMVBoTnlBsAU_jzj5Cpc-xPdGI37ANizZ5DB9gynOHLEy0FZtqgIJlmOkEtqSYHj-Tj8FOFb683UvycH11f_m7u737dXN5cdu5YVCt80GumN0MIDdKKiukB6EV8MFv3EbzddCjdHoQWjMdBvBcyFHIoLwE4MH5YUm-nebOJf_dQ21mm_cl4ZNGSMlWas2ExNR4SrmSay0QzFxwvfJiODNH0WZr3kSbo2jDuEHRyP34j3Ox2aNI3DpO79I_TzSggEPEbnURkgMfC9o2Psd3JvwDfVSioA
CitedBy_id	crossref_primary_10_1007_s12596_024_01868_0 crossref_primary_10_1002_tee_23776 crossref_primary_10_1016_j_solener_2022_03_062 crossref_primary_10_1016_j_apenergy_2025_125370 crossref_primary_10_1016_j_energy_2025_136711 crossref_primary_10_1016_j_rineng_2024_102622 crossref_primary_10_1109_TIM_2024_3379412 crossref_primary_10_1016_j_infrared_2025_105878 crossref_primary_10_3390_en15062055 crossref_primary_10_3390_rs15061686 crossref_primary_10_3390_electronics13244859 crossref_primary_10_1155_2023_6850772 crossref_primary_10_1016_j_solener_2025_113489 crossref_primary_10_1109_JPHOTOV_2024_3492283 crossref_primary_10_1016_j_icheatmasstransfer_2024_107271 crossref_primary_10_1016_j_rser_2021_110889 crossref_primary_10_3390_bioengineering11111076 crossref_primary_10_1016_j_apenergy_2025_126108 crossref_primary_10_1016_j_compeleceng_2024_109872 crossref_primary_10_3390_s22010372 crossref_primary_10_1109_TIM_2022_3212990 crossref_primary_10_7769_gesec_v15i12_4570 crossref_primary_10_1109_ACCESS_2021_3071269 crossref_primary_10_1002_pip_3479 crossref_primary_10_1016_j_egyr_2023_03_094 crossref_primary_10_1002_tee_23647 crossref_primary_10_1016_j_renene_2024_121187 crossref_primary_10_3390_app13042470 crossref_primary_10_3390_s23136235 crossref_primary_10_1016_j_rser_2020_110512 crossref_primary_10_1016_j_solener_2024_112703 crossref_primary_10_1016_j_renene_2025_122926 crossref_primary_10_1016_j_solener_2023_112207 crossref_primary_10_1109_JSEN_2025_3561200 crossref_primary_10_3389_fmed_2025_1551315 crossref_primary_10_1371_journal_pone_0304819 crossref_primary_10_1016_j_rser_2024_114861 crossref_primary_10_3233_JCM_237108 crossref_primary_10_1002_admt_202401013 crossref_primary_10_1002_tee_23996 crossref_primary_10_1109_ACCESS_2021_3117004 crossref_primary_10_1016_j_seta_2025_104441 crossref_primary_10_1016_j_measurement_2023_112466 crossref_primary_10_1016_j_measurement_2024_114683 crossref_primary_10_3390_s24165348 crossref_primary_10_1016_j_cherd_2025_05_030 crossref_primary_10_3390_en16093749 crossref_primary_10_1016_j_eswa_2023_120959 crossref_primary_10_1016_j_engappai_2024_108174 crossref_primary_10_1016_j_eswa_2022_118646 crossref_primary_10_1007_s00521_023_08957_4 crossref_primary_10_1016_j_ijepes_2025_110863 crossref_primary_10_1016_j_engappai_2024_109027 crossref_primary_10_3390_en16104012 crossref_primary_10_3390_s21165668 crossref_primary_10_3390_electronics13132564 crossref_primary_10_1049_ell2_13056 crossref_primary_10_1088_1742_6596_2310_1_012006 crossref_primary_10_3390_su14084832 crossref_primary_10_3390_s25010206 crossref_primary_10_1016_j_renene_2025_124138 crossref_primary_10_1016_j_egyr_2022_03_173 crossref_primary_10_3390_en14196316 crossref_primary_10_3390_en15228667 crossref_primary_10_1016_j_rser_2024_114617 crossref_primary_10_1049_rpg2_12715 crossref_primary_10_1016_j_engappai_2024_107866 crossref_primary_10_1016_j_solener_2025_113672 crossref_primary_10_1016_j_solener_2025_113958 crossref_primary_10_1002_er_7201 crossref_primary_10_1016_j_solener_2025_113959 crossref_primary_10_3390_s23218780 crossref_primary_10_1080_00207543_2023_2232471 crossref_primary_10_1109_ACCESS_2024_3422616 crossref_primary_10_3390_su15119131 crossref_primary_10_3389_fnbot_2024_1396979 crossref_primary_10_1007_s00521_022_07622_6 crossref_primary_10_1016_j_jobe_2023_107285 crossref_primary_10_1016_j_enbuild_2021_111256 crossref_primary_10_1109_TIM_2024_3462989 crossref_primary_10_1016_j_energy_2024_131222 crossref_primary_10_26833_ijeg_1506265 crossref_primary_10_1016_j_asoc_2025_113592 crossref_primary_10_1016_j_segan_2022_100946 crossref_primary_10_1177_1748006X21995388 crossref_primary_10_1016_j_ref_2025_100682 crossref_primary_10_1080_10589759_2025_2537322 crossref_primary_10_5194_gi_11_195_2022 crossref_primary_10_1016_j_solener_2025_113846 crossref_primary_10_12677_MOS_2023_123199 crossref_primary_10_1016_j_eswa_2023_120382 crossref_primary_10_1108_ECAM_07_2021_0631 crossref_primary_10_1155_2024_5586605 crossref_primary_10_1016_j_egyr_2022_10_427 crossref_primary_10_1016_j_seta_2022_102071 crossref_primary_10_1007_s10921_024_01089_2 crossref_primary_10_1016_j_solener_2020_04_052 crossref_primary_10_3390_bioengineering10121430 crossref_primary_10_1595_205651325X17271929252761 crossref_primary_10_1080_10589759_2024_2357240 crossref_primary_10_3390_su15097087 crossref_primary_10_1109_JPHOTOV_2024_3437736 crossref_primary_10_1016_j_solener_2022_05_017 crossref_primary_10_3390_bioengineering11090913 crossref_primary_10_3390_s21134361 crossref_primary_10_3390_forecast5010012 crossref_primary_10_1016_j_energy_2022_125902 crossref_primary_10_1002_tee_70067 crossref_primary_10_1016_j_apenergy_2022_120579 crossref_primary_10_3389_fnbot_2024_1431643 crossref_primary_10_1002_ima_23001 crossref_primary_10_1002_solr_202500289 crossref_primary_10_1016_j_ijleo_2020_165476 crossref_primary_10_1016_j_infrared_2021_103754 crossref_primary_10_1016_j_infrared_2024_105253 crossref_primary_10_1016_j_engappai_2022_105459 crossref_primary_10_1016_j_apenergy_2024_124187 crossref_primary_10_1007_s00521_023_09041_7 crossref_primary_10_1007_s10409_024_24076_x crossref_primary_10_1109_TIM_2021_3126381 crossref_primary_10_1109_ACCESS_2022_3178588 crossref_primary_10_1109_JPHOTOV_2025_3563887 crossref_primary_10_1016_j_sciaf_2025_e02684 crossref_primary_10_1016_j_solener_2020_10_086 crossref_primary_10_3390_s21134292 crossref_primary_10_1109_TIM_2023_3335509 crossref_primary_10_3390_technologies12100175 crossref_primary_10_1016_j_rineng_2025_106491 crossref_primary_10_1109_ACCESS_2021_3111904 crossref_primary_10_1080_0951192X_2021_1901319 crossref_primary_10_1002_ente_202000100 crossref_primary_10_1038_s41598_022_22763_3 crossref_primary_10_1016_j_apenergy_2022_118822 crossref_primary_10_1038_s41598_025_14478_y crossref_primary_10_3390_su14053092 crossref_primary_10_1016_j_solener_2023_112186 crossref_primary_10_1002_pip_3859 crossref_primary_10_1016_j_rser_2025_116057 crossref_primary_10_1016_j_seta_2021_101785 crossref_primary_10_1016_j_solmat_2025_113777 crossref_primary_10_1016_j_infrared_2025_106115 crossref_primary_10_1016_j_seta_2021_101545 crossref_primary_10_1007_s10845_022_02001_3 crossref_primary_10_1109_ACCESS_2024_3509955 crossref_primary_10_1088_1361_6501_ad8e77 crossref_primary_10_3390_a13090207 crossref_primary_10_1002_advs_202302631 crossref_primary_10_1016_j_jii_2024_100760 crossref_primary_10_3390_en16114513 crossref_primary_10_1109_ACCESS_2021_3110947
Cites_doi	10.1049/iet-rpg.2017.0001 10.1109/TEC.2018.2873358 10.1016/j.energy.2018.12.002 10.1016/j.rser.2016.04.079 10.1155/2010/805325 10.1016/j.solmat.2009.09.016 10.1109/EBCCSP.2015.7300708 10.1109/JPHOTOV.2018.2848722 10.1109/WACV.2016.7477658 10.1016/j.solener.2019.02.067 10.1109/POWERCON.2018.8602188 10.1016/j.energy.2019.116319 10.1109/JPHOTOV.2018.2859780 10.1002/ese3.140 10.1016/j.solener.2019.08.061 10.1109/JPHOTOV.2019.2895808 10.1109/JPHOTOV.2018.2846665 10.1109/WACV.2018.00043 10.1109/EEEIC.2012.6221500 10.1002/pip.2717 10.1080/14786451.2013.826223 10.1002/pip.2975 10.1109/ICRERA.2015.7418626 10.3390/en12050769 10.1109/TKDE.2009.191 10.1016/j.solener.2017.03.065 10.1109/TIM.2018.2809078
ContentType	Journal Article
Copyright	2020 International Solar Energy Society Copyright Pergamon Press Inc. Mar 1, 2020
Copyright_xml	– notice: 2020 International Solar Energy Society – notice: Copyright Pergamon Press Inc. Mar 1, 2020
DBID	AAYXX CITATION 7SP 7ST 8FD C1K FR3 KR7 L7M SOI
DOI	10.1016/j.solener.2020.01.055
DatabaseName	CrossRef Electronics & Communications Abstracts Environment Abstracts Technology Research Database Environmental Sciences and Pollution Management Engineering Research Database Civil Engineering Abstracts Advanced Technologies Database with Aerospace Environment Abstracts
DatabaseTitle	CrossRef Civil Engineering Abstracts Technology Research Database Electronics & Communications Abstracts Engineering Research Database Environment Abstracts Advanced Technologies Database with Aerospace Environmental Sciences and Pollution Management
DatabaseTitleList	Civil Engineering Abstracts
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1471-1257
EndPage	186
ExternalDocumentID	10_1016_j_solener_2020_01_055 S0038092X20300621
GroupedDBID	--K --M -ET -~X .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 5VS 7-5 71M 8P~ 9JN AABNK AABXZ AACTN AAEDT AAEDW AAEPC AAHCO AAIAV AAIKC AAIKJ AAKOC AALRI AAMNW AAOAW AAQFI AARJD AAXUO ABMAC ABXRA ABYKQ ACDAQ ACGFS ACGOD ACIWK ACRLP ADBBV ADEZE ADHUB AEBSH AEKER AENEX AEZYN AFKWA AFRAH AFRZQ AFTJW AGHFR AGUBO AGYEJ AHHHB AHIDL AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BELTK BKOJK BKOMP BLXMC CS3 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA H~9 IHE J1W JARJE KOM LY6 M41 MAGPM MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ RXW SDF SDG SDP SES SPC SPCBC SSM SSR SSZ T5K TAE TN5 WH7 XPP YNT ZMT ~02 ~G- ~KM ~S- 6TJ 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABDPE ABFNM ABJNI ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP AZFZN CITATION EFKBS EJD FEDTE FGOYB G-2 HVGLF HZ~ NEJ R2- SAC SEW UKR VOH WUQ XOL ZY4 ~A~ ~HD 7SP 7ST 8FD AFXIZ AGCQF AGRNS BNPGV C1K FR3 KR7 L7M SOI
ID	FETCH-LOGICAL-c337t-df460ab3e4b747a24de287e13dbcb819f854c8328808f3ed124524f7d4ee1fcd3
ISICitedReferencesCount	181
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000524527300016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0038-092X
IngestDate	Sat Jul 26 02:33:25 EDT 2025 Sat Nov 29 07:28:26 EST 2025 Tue Nov 18 22:04:07 EST 2025 Fri Feb 23 02:48:44 EST 2024
IsPeerReviewed	true
IsScholarly	true
Keywords	Isolated deep learning Automatic defect detection Develop-model transfer deep learning Photovoltaic (PV) modules Infrared images Thermography
Language	English
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c337t-df460ab3e4b747a24de287e13dbcb819f854c8328808f3ed124524f7d4ee1fcd3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-5559-043X
PQID	2440679024
PQPubID	9393
PageCount	12
ParticipantIDs	proquest_journals_2440679024 crossref_primary_10_1016_j_solener_2020_01_055 crossref_citationtrail_10_1016_j_solener_2020_01_055 elsevier_sciencedirect_doi_10_1016_j_solener_2020_01_055
PublicationCentury	2000
PublicationDate	2020-03-01 2020-03-00 20200301
PublicationDateYYYYMMDD	2020-03-01
PublicationDate_xml	– month: 03 year: 2020 text: 2020-03-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	Solar energy
PublicationYear	2020
Publisher	Elsevier Ltd Pergamon Press Inc
Publisher_xml	– name: Elsevier Ltd – name: Pergamon Press Inc
References	Tsanakas, Chrysostomou, Botsaris, Gasteratos (b0230) 2015; 34 Hoyer, U., Buerhop, C., Jahn, U., 2008. Electroluminescence and infrared imaging for quality improvements of PV modules. In: 23rd European Photovoltaic Solar Energy Conference and Exhibition (EU-PVSEC). pp. 2913–2916. Li, X., Yang, Q., Wang, J., Chen, Z., Yan, W., 2018. Intelligent fault pattern recognition of aerial photovoltaic module images based on deep learning technique. In: 9th International Multi-Conference on Complexity, Informatics and Cybernetics (IMCIC 2018). pp. 22–27. Bedrich, Luo, Pravettoni, Chen, Chen, Wang, Verlinden, Hacke, Feng, Chai, Wang, Aberle, Khoo (b0025) 2018; 8 Li, Akram, Jin, Chen, Zhu, Ahmad, Arshad, Zhao (b0125) 2019; 168 Tománek, Škarvada, Macků, Grmela (b0225) 2010; 2010 Brownlee (b0030) 2019 Akram, Li, Jin, Chen, Zhu, Zhao, Aleem, Ahmad (b0015) 2019; 190 Simonyan, Zisserman (b0195) 2015 Yosinski, J., Clune, J., Bengio, Y., Lipson, H., 2014. How transferable are features in deep neural networks ?. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. MIT Press Cambridge, MA, USA, pp. 3320–3328. Jahn, U., Herz, M., Köntges, M., Parlevliet, D., Paggi, M., Tsanakas, I., Stein, J.S., Berger, K.A., Ranta, S., French, R.H., Richter, M., Tanahashi, T., 2018. Performance and Reliability of Photovoltaic Systems – subtask 3.3: Review on Infrared and Electroluminescence Imaging for PV Field Applications. External final report by international energy agency (IEA) for photovoltaic power systems programme (PVPS). Buerhop, C., Deitsch, S., Maier, A., Gallwitz, F., Berger, S., Doll, B., Hauch, J., Camus, C., Brabec, C.J., Bayern, V., Erlangen, D.-, Nürnberg, H., Simon, G., Nürnberg, E.C., Straße, F., Mustererkennung, L., Erlangen-nürnberg, F.A.U., Erlangen-nürnberg, F.A.U., Erlangen, D., 2018a. A benchmark for visual identification of defective solar cells in electroluminescence imagery. In: 35th European PV Solar Energy Conference and Exhibition. pp. 1287–1289. Li, Yang, Chen, Luo, Yan (b0130) 2017; 11 Géron (b0095) 2017 Pan, Fellow (b0155) 2010; 22 Demant, M., Virtue, P., Kovvali, A.S., Yu, S.X., Rein., S., 2018. Deep learning approach to inline quality rating and mapping of multi-crystalline Si-wafers. In: 35th European Photovoltaic Solar Energy Conference and Exhibition (35th EU PVSEC). pp. 814–818. Aghaei, M., Gandelli, A., Grimaccia, F., Leva, S., Zich, R.E., 2015. IR real-time analyses for PV system monitoring by digital image processing techniques. In: IEEE International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP). IEEE, pp. 1–6. Deitsch, S., Christlein, V., Berger, S., Buerhop-Lutz, C., Maier, A., Gallwitz, F., & Riess, C., 2019. Automatic classification of defective photovoltaic module cells in electroluminescence images. arXiv:1807.02894v3. Chollet (b0060) 2018 . Stromer, Vetter, Oezkan, Probst, Maier (b0205) 2019; 9 Buerhop, Wirsching, Bemm, Pickel, Hohmann, Nieß, Vodermayer, Huber, Glück, Mergheim, Camus, Hauch, Brabec (b0045) 2018; 26 Jordan, Silverman, Wohlgemuth, Kurtz, VanSant (b0085) 2017 Vergura, S., Marino, F., Carpentieri, M., 2015. Processing infrared image of PV modules for defects classification. In: 4th IEEE International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, pp. 1337–1341. Li, Yang, Lou, Yan (b0135) 2019; 34 Jäger-Waldau (b0110) 2019; 12 Tang, Y., 2015. Deep learning using linear support vector machines. arXiv:1306.0239v4. Köntges, M., Sarah, K., Packard, C., Jahn, U., Berger, K.A., Kato, K., Friesen, T., Liu, H., Iseghe, M. Van, 2014. performance and reliability of photovoltcaic systems – subtask 3.2: review of failures of photovoltaic modules. External final report by international energy agency (IEA) for photovoltaic power systems programme (PVPS). Deitsch, S., Buerhop-lutz, C., Maier, A., Gallwitz, F., Riess, C., 2018. Segmentation of photovoltaic module cells in electroluminescence images. arXiv:1806.06530v2. Hepp, Machui, Egelhaaf, Brabec, Vetter (b0100) 2016; 4 Pozza, Sample (b0165) 2016; 24 Ding, S., Yang, Q., Li, X., Yan, W., Ruan, W., 2018. Transfer learning based photovoltaic module defect diagnosis using aerial images. In: 2018 International Conference on Power System Technology (POWERCON). IEEE, pp. 4245–4250. Dotenco, S., Dalsass, M., Winkler, L., Würzner, T., Brabec, C., Maier, A., Gallwitz, F., Bayern, Z.A.E., 2016. Automatic detection and analysis of photovoltaic modules in aerial infrared imagery. In: IEEE Winter Conference on Applications of Computer Vision (WACV). Lake Placid, New York, USA. Schuss, Remes, Leppänen, Saarela, Fabritius, Eichberger, Rahkonen (b0180) 2018; 67 Ram, M., Bogdanov, D., Aghahosseini, A., Gulagi, A., Oyewo, A.S., Child, M., Caldera, U., Sadovskaia, K., Farfan, J., Barbosa, L.S.N.S., Fasihi, M., Khalili, S., Dalheimer, B., Gruber, G., Traber, T., De Caluwe, F., Fell, H.-J., B.C., 2019. Global energy system based on 100% renewable energy - power, heat, transport and desalination sectors. Study by Lappeenranta University of Technology and Energy Watch Group, Lappeenranta, Berlin. Su, Chen, Zhu, Liu, Liu (b0210) 2019; PP WEO, 2018. 2018 World Energy Outlook, International Energy Agency. https://doi.org/ISBN PDF 978-92-64-30677-6 (ISBN Print 978-92-64-06452-2). Chen, Pang, Hu, Liu (b0055) 2018 Sarkar, Bali, Ghosh (b0175) 2018 Buerhop, Fecher, Pickel, Häring, Adamski, Brabec (b0040) 2018 Akram, Li, Jin, Chen, Zhu, Zhao (b0010) 2019 Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C., 2018. A survey on deep transfer learning. arXiv:1808.01974v1 1–10. Mehta, S., Azad, A.P., Chemmengath, S.A., Raykar, V., 2018. Deep solar eye : power loss prediction and weakly supervised soiling localization via fully convolutional networks for solar panels. In: 2018 IEEE Winter Conf. Appl. Comput. Vis. 333–342. https://doi.org/10.1109/WACV.2018.00043. Tsanakas, Ha, Buerhop (b0235) 2016; 62 Chattopadhyay, Dubey, Bhaduri, Zachariah, Singh, Solanki, Kottantharayil, Shiradkar, Arora, Narasimhan, Vasi (b0050) 2018; 8 Simon, Meyer (b0190) 2010; 94 Tsanakas, J.A., Vannier, G., Plissonnier, A., Ha, L.D., Barruel, F., 2015. Fault diagnosis and classification of large-scale photovoltaic plants through aerial orthophoto thermal mapping. In: 31st European Photovoltaic Solar Energy Conference and Exhibition. pp. 1783–1788. Demant, M., Welschehold, T., Nold, S., 2014. Micro-cracks in silicon wafers and solar cells: detection and rating of mechanical strength and electrical quality. In: 29th European PV Solar Energy Conference and Exhibition. Nasr, Asghari, Rashid-nadimi (b0150) 2017; 148 Bedrich, Bokalič, Bliss, Topič, Betts, Gottschalg (b0020) 2018; 8 Petrosyan, A., Hovhannisyan, A., 2017. Infrared image processing for solar cell defect detection. In: IEEE 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). IEEE. Spagnolo, G.S., Vecchio, P. Del, Makary, G., Papalillo, D., Martocchia, A., 2012. A review of IR thermography applied to PV systems. In: 11th IEEE International Conference on Environment and Electrical Engineering (EEEIC). IEEE, pp. 879–884. 10.1016/j.solener.2020.01.055_b0035 Jordan (10.1016/j.solener.2020.01.055_b0085) 2017 10.1016/j.solener.2020.01.055_b0115 Akram (10.1016/j.solener.2020.01.055_b0015) 2019; 190 Hepp (10.1016/j.solener.2020.01.055_b0100) 2016; 4 Stromer (10.1016/j.solener.2020.01.055_b0205) 2019; 9 Li (10.1016/j.solener.2020.01.055_b0125) 2019; 168 Pan (10.1016/j.solener.2020.01.055_b0155) 2010; 22 Tsanakas (10.1016/j.solener.2020.01.055_b0235) 2016; 62 10.1016/j.solener.2020.01.055_b0070 Buerhop (10.1016/j.solener.2020.01.055_b0040) 2018 10.1016/j.solener.2020.01.055_b0075 Li (10.1016/j.solener.2020.01.055_b0130) 2017; 11 Tománek (10.1016/j.solener.2020.01.055_b0225) 2010; 2010 10.1016/j.solener.2020.01.055_b0145 Nasr (10.1016/j.solener.2020.01.055_b0150) 2017; 148 10.1016/j.solener.2020.01.055_b0220 10.1016/j.solener.2020.01.055_b0105 Bedrich (10.1016/j.solener.2020.01.055_b0020) 2018; 8 Pozza (10.1016/j.solener.2020.01.055_b0165) 2016; 24 Géron (10.1016/j.solener.2020.01.055_b0095) 2017 Li (10.1016/j.solener.2020.01.055_b0135) 2019; 34 Tsanakas (10.1016/j.solener.2020.01.055_b0230) 2015; 34 Akram (10.1016/j.solener.2020.01.055_b0010) 2019 10.1016/j.solener.2020.01.055_b0065 Su (10.1016/j.solener.2020.01.055_b0210) 2019; PP 10.1016/j.solener.2020.01.055_b0185 Bedrich (10.1016/j.solener.2020.01.055_b0025) 2018; 8 10.1016/j.solener.2020.01.055_b0140 10.1016/j.solener.2020.01.055_b0255 10.1016/j.solener.2020.01.055_b0215 Simonyan (10.1016/j.solener.2020.01.055_b0195) 2015 Jäger-Waldau (10.1016/j.solener.2020.01.055_b0110) 2019; 12 Chollet (10.1016/j.solener.2020.01.055_b0060) 2018 Sarkar (10.1016/j.solener.2020.01.055_b0175) 2018 Schuss (10.1016/j.solener.2020.01.055_b0180) 2018; 67 10.1016/j.solener.2020.01.055_b0090 10.1016/j.solener.2020.01.055_b0170 Chen (10.1016/j.solener.2020.01.055_b0055) 2018 10.1016/j.solener.2020.01.055_b0250 Chattopadhyay (10.1016/j.solener.2020.01.055_b0050) 2018; 8 10.1016/j.solener.2020.01.055_b0245 10.1016/j.solener.2020.01.055_b0200 10.1016/j.solener.2020.01.055_b0005 Buerhop (10.1016/j.solener.2020.01.055_b0045) 2018; 26 Brownlee (10.1016/j.solener.2020.01.055_b0030) 2019 10.1016/j.solener.2020.01.055_b0160 Simon (10.1016/j.solener.2020.01.055_b0190) 2010; 94 10.1016/j.solener.2020.01.055_b0080 10.1016/j.solener.2020.01.055_b0120 10.1016/j.solener.2020.01.055_b0240
References_xml	– reference: Dotenco, S., Dalsass, M., Winkler, L., Würzner, T., Brabec, C., Maier, A., Gallwitz, F., Bayern, Z.A.E., 2016. Automatic detection and analysis of photovoltaic modules in aerial infrared imagery. In: IEEE Winter Conference on Applications of Computer Vision (WACV). Lake Placid, New York, USA. – volume: 8 start-page: 1297 year: 2018 end-page: 1304 ident: b0020 article-title: Electroluminescence imaging of PV devices: advanced vignetting calibration publication-title: IEEE J. Photovoltaics – start-page: 1 year: 2018 end-page: 9 ident: b0040 article-title: Verifying defective PV – modules by IR-imaging and controlling with module optimizers publication-title: Prog. Photovoltaics Res. Appl. – reference: Yosinski, J., Clune, J., Bengio, Y., Lipson, H., 2014. How transferable are features in deep neural networks ?. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. MIT Press Cambridge, MA, USA, pp. 3320–3328. – reference: Petrosyan, A., Hovhannisyan, A., 2017. Infrared image processing for solar cell defect detection. In: IEEE 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). IEEE. – volume: 11 start-page: 1234 year: 2017 end-page: 1244 ident: b0130 article-title: Visible defects detection based on UAV-based inspection in large-scale photovoltaic systems publication-title: IET Renew. Power Gener. – volume: 67 start-page: 1178 year: 2018 end-page: 1186 ident: b0180 article-title: Detecting defects in photovoltaic modules with the help of experimental verification and synchronized thermography publication-title: IEEE Trans. Instrum. Meas. – volume: PP start-page: 1 year: 2019 end-page: 14 ident: b0210 article-title: Classification of manufacturing defects in multicrystalline solar cells with novel feature descriptor publication-title: IEEE Trans. Instrum. Meas. – year: 2019 ident: b0030 article-title: Deep Learning for Computer Vision: Image Classification, Object Detection and Face Recognition in Python – reference: WEO, 2018. 2018 World Energy Outlook, International Energy Agency. https://doi.org/ISBN PDF 978-92-64-30677-6 (ISBN Print 978-92-64-06452-2). – reference: Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C., 2018. A survey on deep transfer learning. arXiv:1808.01974v1 1–10. – volume: 24 start-page: 368 year: 2016 end-page: 378 ident: b0165 article-title: Crystalline silicon PV module degradation after 20years of field exposure studied by electrical tests, electroluminescence, and LBIC publication-title: Prog. Photovoltaics Res. Appl. – reference: Ram, M., Bogdanov, D., Aghahosseini, A., Gulagi, A., Oyewo, A.S., Child, M., Caldera, U., Sadovskaia, K., Farfan, J., Barbosa, L.S.N.S., Fasihi, M., Khalili, S., Dalheimer, B., Gruber, G., Traber, T., De Caluwe, F., Fell, H.-J., B.C., 2019. Global energy system based on 100% renewable energy - power, heat, transport and desalination sectors. Study by Lappeenranta University of Technology and Energy Watch Group, Lappeenranta, Berlin. – volume: 34 start-page: 520 year: 2019 end-page: 529 ident: b0135 article-title: Deep learning based module defect analysis for large-scale photovoltaic farms publication-title: IEEE Trans. Energy Convers. – volume: 8 start-page: 1800 year: 2018 end-page: 1808 ident: b0050 article-title: Correlating infrared thermography with electrical degradation of PV modules inspected in All-India survey of photovoltaic module reliability 2016 publication-title: IEEE J. Photovoltaics – reference: Demant, M., Welschehold, T., Nold, S., 2014. Micro-cracks in silicon wafers and solar cells: detection and rating of mechanical strength and electrical quality. In: 29th European PV Solar Energy Conference and Exhibition. – year: 2018 ident: b0175 article-title: Hands-On Transfer Learning with Python: Implement Advanced Deep Learning and Neural Network Models using TensorFlow and Keras – volume: 62 start-page: 695 year: 2016 end-page: 709 ident: b0235 article-title: Faults and infrared thermographic diagnosis in operating c-Si photovoltaic modules: a review of research and future challenges publication-title: Renew. Sustain. Energy Rev. – reference: Demant, M., Virtue, P., Kovvali, A.S., Yu, S.X., Rein., S., 2018. Deep learning approach to inline quality rating and mapping of multi-crystalline Si-wafers. In: 35th European Photovoltaic Solar Energy Conference and Exhibition (35th EU PVSEC). pp. 814–818. – volume: 148 start-page: 49 year: 2017 end-page: 56 ident: b0150 article-title: A numerical model for soldering process in silicon solar cells publication-title: Sol. Energy – volume: 9 start-page: 752 year: 2019 end-page: 758 ident: b0205 article-title: Enhanced Crack Segmentation (eCS): a reference algorithm for segmenting cracks in multicrystalline silicon solar cells publication-title: IEEE J. Photovoltaics – reference: Ding, S., Yang, Q., Li, X., Yan, W., Ruan, W., 2018. Transfer learning based photovoltaic module defect diagnosis using aerial images. In: 2018 International Conference on Power System Technology (POWERCON). IEEE, pp. 4245–4250. – volume: 26 start-page: 261 year: 2018 end-page: 272 ident: b0045 article-title: Evolution of cell cracks in PV-modules under field and laboratory conditions publication-title: Prog. Photovoltaics Res. Appl. – year: 2019 ident: b0010 article-title: CNN based automatic detection of photovoltaic cell defects in electroluminescence images publication-title: Energy – year: 2018 ident: b0055 article-title: Solar cell surface defect inspection based on multispectral convolutional neural network publication-title: J. Intell. Manuf. – reference: Deitsch, S., Christlein, V., Berger, S., Buerhop-Lutz, C., Maier, A., Gallwitz, F., & Riess, C., 2019. Automatic classification of defective photovoltaic module cells in electroluminescence images. arXiv:1807.02894v3. – volume: 168 start-page: 931 year: 2019 end-page: 945 ident: b0125 article-title: Thermo-mechanical behavior assessment of smart wire connected and busbarPV modules during production, transportation, and subsequent field loading stages publication-title: Energy – reference: Köntges, M., Sarah, K., Packard, C., Jahn, U., Berger, K.A., Kato, K., Friesen, T., Liu, H., Iseghe, M. Van, 2014. performance and reliability of photovoltcaic systems – subtask 3.2: review of failures of photovoltaic modules. External final report by international energy agency (IEA) for photovoltaic power systems programme (PVPS). – volume: 2010 start-page: 1 year: 2010 end-page: 5 ident: b0225 article-title: Detection and localization of defects in monocrystalline silicon solar cell publication-title: Adv. Opt. Technol. – year: 2018 ident: b0060 article-title: Deep Learning with Python – year: 2017 ident: b0095 article-title: Hands-On Machine Learning with Scikit-Learn and TensorFlow – volume: 12 year: 2019 ident: b0110 article-title: Snapshot of photovoltaics—February 2019 publication-title: Energies – volume: 190 start-page: 549 year: 2019 end-page: 560 ident: b0015 article-title: Improved outdoor thermography and processing of infrared images for defect detection in PV modules publication-title: Sol. Energy – volume: 22 start-page: 1345 year: 2010 end-page: 1359 ident: b0155 article-title: A Survey on Transfer Learning publication-title: IEEE Trans. Knowl. Data Eng. – volume: 4 start-page: 363 year: 2016 end-page: 371 ident: b0100 article-title: Automatized analysis of IR- images of photovoltaic modules and its use for quality control of solar cells publication-title: Energy Sci. Eng. – reference: Jahn, U., Herz, M., Köntges, M., Parlevliet, D., Paggi, M., Tsanakas, I., Stein, J.S., Berger, K.A., Ranta, S., French, R.H., Richter, M., Tanahashi, T., 2018. Performance and Reliability of Photovoltaic Systems – subtask 3.3: Review on Infrared and Electroluminescence Imaging for PV Field Applications. External final report by international energy agency (IEA) for photovoltaic power systems programme (PVPS). – volume: 94 start-page: 106 year: 2010 end-page: 113 ident: b0190 article-title: Detection and analysis of hot-spot formation in solar cells publication-title: Sol. Energy Mater. Sol. Cells – reference: Tang, Y., 2015. Deep learning using linear support vector machines. arXiv:1306.0239v4. – reference: Aghaei, M., Gandelli, A., Grimaccia, F., Leva, S., Zich, R.E., 2015. IR real-time analyses for PV system monitoring by digital image processing techniques. In: IEEE International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP). IEEE, pp. 1–6. – volume: 8 start-page: 1281 year: 2018 end-page: 1288 ident: b0025 article-title: Quantitative electroluminescence imaging analysis for performance estimation of PID-influenced PV modules publication-title: IEEE J. Photovoltaics – reference: Vergura, S., Marino, F., Carpentieri, M., 2015. Processing infrared image of PV modules for defects classification. In: 4th IEEE International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, pp. 1337–1341. – reference: Deitsch, S., Buerhop-lutz, C., Maier, A., Gallwitz, F., Riess, C., 2018. Segmentation of photovoltaic module cells in electroluminescence images. arXiv:1806.06530v2. – reference: Buerhop, C., Deitsch, S., Maier, A., Gallwitz, F., Berger, S., Doll, B., Hauch, J., Camus, C., Brabec, C.J., Bayern, V., Erlangen, D.-, Nürnberg, H., Simon, G., Nürnberg, E.C., Straße, F., Mustererkennung, L., Erlangen-nürnberg, F.A.U., Erlangen-nürnberg, F.A.U., Erlangen, D., 2018a. A benchmark for visual identification of defective solar cells in electroluminescence imagery. In: 35th European PV Solar Energy Conference and Exhibition. pp. 1287–1289. – reference: Li, X., Yang, Q., Wang, J., Chen, Z., Yan, W., 2018. Intelligent fault pattern recognition of aerial photovoltaic module images based on deep learning technique. In: 9th International Multi-Conference on Complexity, Informatics and Cybernetics (IMCIC 2018). pp. 22–27. – reference: Mehta, S., Azad, A.P., Chemmengath, S.A., Raykar, V., 2018. Deep solar eye : power loss prediction and weakly supervised soiling localization via fully convolutional networks for solar panels. In: 2018 IEEE Winter Conf. Appl. Comput. Vis. 333–342. https://doi.org/10.1109/WACV.2018.00043. – reference: . – year: 2015 ident: b0195 article-title: Very deep convolutional networks for large-scale image recognition publication-title: International Conference on Learning Representations – reference: Spagnolo, G.S., Vecchio, P. Del, Makary, G., Papalillo, D., Martocchia, A., 2012. A review of IR thermography applied to PV systems. In: 11th IEEE International Conference on Environment and Electrical Engineering (EEEIC). IEEE, pp. 879–884. – year: 2017 ident: b0085 article-title: Photovoltaic failure and degradation modes publication-title: Prog. Photovoltaics Res. Appl. – volume: 34 start-page: 351 year: 2015 end-page: 372 ident: b0230 article-title: Fault diagnosis of photovoltaic modules through image processing and Canny edge detection on field thermographic measurements publication-title: Int. J. Sustain. Energy – reference: Hoyer, U., Buerhop, C., Jahn, U., 2008. Electroluminescence and infrared imaging for quality improvements of PV modules. In: 23rd European Photovoltaic Solar Energy Conference and Exhibition (EU-PVSEC). pp. 2913–2916. – reference: Tsanakas, J.A., Vannier, G., Plissonnier, A., Ha, L.D., Barruel, F., 2015. Fault diagnosis and classification of large-scale photovoltaic plants through aerial orthophoto thermal mapping. In: 31st European Photovoltaic Solar Energy Conference and Exhibition. pp. 1783–1788. – volume: 11 start-page: 1234 year: 2017 ident: 10.1016/j.solener.2020.01.055_b0130 article-title: Visible defects detection based on UAV-based inspection in large-scale photovoltaic systems publication-title: IET Renew. Power Gener. doi: 10.1049/iet-rpg.2017.0001 – ident: 10.1016/j.solener.2020.01.055_b0075 – year: 2018 ident: 10.1016/j.solener.2020.01.055_b0060 – year: 2018 ident: 10.1016/j.solener.2020.01.055_b0055 article-title: Solar cell surface defect inspection based on multispectral convolutional neural network publication-title: J. Intell. Manuf. – volume: 34 start-page: 520 year: 2019 ident: 10.1016/j.solener.2020.01.055_b0135 article-title: Deep learning based module defect analysis for large-scale photovoltaic farms publication-title: IEEE Trans. Energy Convers. doi: 10.1109/TEC.2018.2873358 – volume: 168 start-page: 931 year: 2019 ident: 10.1016/j.solener.2020.01.055_b0125 article-title: Thermo-mechanical behavior assessment of smart wire connected and busbarPV modules during production, transportation, and subsequent field loading stages publication-title: Energy doi: 10.1016/j.energy.2018.12.002 – volume: 62 start-page: 695 year: 2016 ident: 10.1016/j.solener.2020.01.055_b0235 article-title: Faults and infrared thermographic diagnosis in operating c-Si photovoltaic modules: a review of research and future challenges publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2016.04.079 – ident: 10.1016/j.solener.2020.01.055_b0255 – volume: PP start-page: 1 year: 2019 ident: 10.1016/j.solener.2020.01.055_b0210 article-title: Classification of manufacturing defects in multicrystalline solar cells with novel feature descriptor publication-title: IEEE Trans. Instrum. Meas. – volume: 2010 start-page: 1 year: 2010 ident: 10.1016/j.solener.2020.01.055_b0225 article-title: Detection and localization of defects in monocrystalline silicon solar cell publication-title: Adv. Opt. Technol. doi: 10.1155/2010/805325 – year: 2018 ident: 10.1016/j.solener.2020.01.055_b0175 – volume: 94 start-page: 106 year: 2010 ident: 10.1016/j.solener.2020.01.055_b0190 article-title: Detection and analysis of hot-spot formation in solar cells publication-title: Sol. Energy Mater. Sol. Cells doi: 10.1016/j.solmat.2009.09.016 – ident: 10.1016/j.solener.2020.01.055_b0215 – ident: 10.1016/j.solener.2020.01.055_b0240 – ident: 10.1016/j.solener.2020.01.055_b0250 – ident: 10.1016/j.solener.2020.01.055_b0005 doi: 10.1109/EBCCSP.2015.7300708 – volume: 8 start-page: 1297 year: 2018 ident: 10.1016/j.solener.2020.01.055_b0020 article-title: Electroluminescence imaging of PV devices: advanced vignetting calibration publication-title: IEEE J. Photovoltaics doi: 10.1109/JPHOTOV.2018.2848722 – ident: 10.1016/j.solener.2020.01.055_b0070 – year: 2017 ident: 10.1016/j.solener.2020.01.055_b0095 – ident: 10.1016/j.solener.2020.01.055_b0120 – ident: 10.1016/j.solener.2020.01.055_b0090 doi: 10.1109/WACV.2016.7477658 – ident: 10.1016/j.solener.2020.01.055_b0185 doi: 10.1016/j.solener.2019.02.067 – year: 2015 ident: 10.1016/j.solener.2020.01.055_b0195 article-title: Very deep convolutional networks for large-scale image recognition – ident: 10.1016/j.solener.2020.01.055_b0220 – ident: 10.1016/j.solener.2020.01.055_b0080 doi: 10.1109/POWERCON.2018.8602188 – ident: 10.1016/j.solener.2020.01.055_b0115 – ident: 10.1016/j.solener.2020.01.055_b0140 – year: 2017 ident: 10.1016/j.solener.2020.01.055_b0085 article-title: Photovoltaic failure and degradation modes publication-title: Prog. Photovoltaics Res. Appl. – year: 2019 ident: 10.1016/j.solener.2020.01.055_b0010 article-title: CNN based automatic detection of photovoltaic cell defects in electroluminescence images publication-title: Energy doi: 10.1016/j.energy.2019.116319 – ident: 10.1016/j.solener.2020.01.055_b0035 – ident: 10.1016/j.solener.2020.01.055_b0105 – volume: 8 start-page: 1800 year: 2018 ident: 10.1016/j.solener.2020.01.055_b0050 article-title: Correlating infrared thermography with electrical degradation of PV modules inspected in All-India survey of photovoltaic module reliability 2016 publication-title: IEEE J. Photovoltaics doi: 10.1109/JPHOTOV.2018.2859780 – volume: 4 start-page: 363 year: 2016 ident: 10.1016/j.solener.2020.01.055_b0100 article-title: Automatized analysis of IR- images of photovoltaic modules and its use for quality control of solar cells publication-title: Energy Sci. Eng. doi: 10.1002/ese3.140 – volume: 190 start-page: 549 year: 2019 ident: 10.1016/j.solener.2020.01.055_b0015 article-title: Improved outdoor thermography and processing of infrared images for defect detection in PV modules publication-title: Sol. Energy doi: 10.1016/j.solener.2019.08.061 – volume: 9 start-page: 752 year: 2019 ident: 10.1016/j.solener.2020.01.055_b0205 article-title: Enhanced Crack Segmentation (eCS): a reference algorithm for segmenting cracks in multicrystalline silicon solar cells publication-title: IEEE J. Photovoltaics doi: 10.1109/JPHOTOV.2019.2895808 – volume: 8 start-page: 1281 year: 2018 ident: 10.1016/j.solener.2020.01.055_b0025 article-title: Quantitative electroluminescence imaging analysis for performance estimation of PID-influenced PV modules publication-title: IEEE J. Photovoltaics doi: 10.1109/JPHOTOV.2018.2846665 – ident: 10.1016/j.solener.2020.01.055_b0145 doi: 10.1109/WACV.2018.00043 – ident: 10.1016/j.solener.2020.01.055_b0200 doi: 10.1109/EEEIC.2012.6221500 – volume: 24 start-page: 368 year: 2016 ident: 10.1016/j.solener.2020.01.055_b0165 article-title: Crystalline silicon PV module degradation after 20years of field exposure studied by electrical tests, electroluminescence, and LBIC publication-title: Prog. Photovoltaics Res. Appl. doi: 10.1002/pip.2717 – ident: 10.1016/j.solener.2020.01.055_b0160 – volume: 34 start-page: 351 year: 2015 ident: 10.1016/j.solener.2020.01.055_b0230 article-title: Fault diagnosis of photovoltaic modules through image processing and Canny edge detection on field thermographic measurements publication-title: Int. J. Sustain. Energy doi: 10.1080/14786451.2013.826223 – volume: 26 start-page: 261 year: 2018 ident: 10.1016/j.solener.2020.01.055_b0045 article-title: Evolution of cell cracks in PV-modules under field and laboratory conditions publication-title: Prog. Photovoltaics Res. Appl. doi: 10.1002/pip.2975 – start-page: 1 year: 2018 ident: 10.1016/j.solener.2020.01.055_b0040 article-title: Verifying defective PV – modules by IR-imaging and controlling with module optimizers publication-title: Prog. Photovoltaics Res. Appl. – ident: 10.1016/j.solener.2020.01.055_b0065 doi: 10.1016/j.solener.2019.02.067 – ident: 10.1016/j.solener.2020.01.055_b0245 doi: 10.1109/ICRERA.2015.7418626 – volume: 12 year: 2019 ident: 10.1016/j.solener.2020.01.055_b0110 article-title: Snapshot of photovoltaics—February 2019 publication-title: Energies doi: 10.3390/en12050769 – volume: 22 start-page: 1345 year: 2010 ident: 10.1016/j.solener.2020.01.055_b0155 article-title: A Survey on Transfer Learning publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2009.191 – year: 2019 ident: 10.1016/j.solener.2020.01.055_b0030 – volume: 148 start-page: 49 year: 2017 ident: 10.1016/j.solener.2020.01.055_b0150 article-title: A numerical model for soldering process in silicon solar cells publication-title: Sol. Energy doi: 10.1016/j.solener.2017.03.065 – volume: 67 start-page: 1178 year: 2018 ident: 10.1016/j.solener.2020.01.055_b0180 article-title: Detecting defects in photovoltaic modules with the help of experimental verification and synchronized thermography publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2018.2809078 – ident: 10.1016/j.solener.2020.01.055_b0170
SSID	ssj0017187
Score	2.649999
Snippet	[Display omitted] •State-of-the-art framework is proposed for automatic defect detection in PV modules.•Infrared images dataset of normal operating and... With the rising use of photovoltaic and ongoing installation of large-scale photovoltaic systems worldwide, the automation of photovoltaic monitoring methods...
SourceID	proquest crossref elsevier
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	175
SubjectTerms	Accuracy Artificial neural networks Automatic defect detection Automation Datasets Deep learning Defects Develop-model transfer deep learning Electroluminescence Infrared imagery Infrared images Infrared imaging Inspection Isolated deep learning Labeling Machine learning Modules Monitoring methods Neural networks Performance enhancement Photovoltaic (PV) modules Photovoltaic cells Photovoltaics Solar energy Thermography Transfer learning
Title	Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning
URI	https://dx.doi.org/10.1016/j.solener.2020.01.055 https://www.proquest.com/docview/2440679024
Volume	198
WOSCitedRecordID	wos000524527300016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1471-1257 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017187 issn: 0038-092X databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEF5FKQc4IJ6iUNAeuFUOttex18cIlZegQmoR4bSy91FcWjtNnKoH_gl_ltmnUx4qIHGxIkd2VpnPM9-Ov5lB6KmidU5qISIF4STK8jiLylzkkUqBytG8gLDBzbCJYn-fzufl-9Hom6-FOT8p2pZeXJSL_2pqOAfG1qWzf2HucFM4AZ_B6HAEs8Pxjww_W_ed7cMqZC-5Z4SLz13fgS_qK_jmtBNrUzFlxRyNljuqpdGiN6eVbvtg8rMNrLTSlFRn1115VWRm5-jREkB4zXhxufCzJ442qe6B3jTvSlNbGGD1ZWkB-G6y-7E6q4Iy-K0RFbxcN2cA16Mg67ENDj41gwjBesl5U3Wb-QrYnAbBlk2i-UKaQbVkHDPRKgwzWR3CkvXFEDcj4F_FJWdtZ1Y7d5vYqSsucie2qfZPQcHmJ44nK610kLoJbBqbXq3T6RAFgzbxQK9FLyUF_xfnuknBVlpMSzpGW7PXe_M34SUVhHXbktWtfSgQe_bLH_sd9fmBBBhmc3gL3XRbEjyzULqNRrK9g25sNKq8i74GUOEAKtwpvAkqbEGFHahw02IPKmxBhTWosAcVBlDhS6DCHlRYgwp7UN1DH17sHT5_Fbm5HREnpOgjoeCZr2oisxo2q1WaCQn7cpkQUfMaGKii04xDJIHQQRWRItFv_zNViEzKRHFB7qNx27XyAcKwoS1FmYIjkQqiS0olSUuZxrKIOU-I2kaZ_0sZd03t9WyVE-bVi8fMWYJpS7A4YWCJbTQJly1sV5erLqDeXsxRU0s5GYDsqkt3vH2ZcxMrBqRaZ3CBID_89zs_QteHB2wHjfvlWj5G1_h536yWTxxavwNduMRu
linkProvider	Elsevier
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Automatic+detection+of+photovoltaic+module+defects+in+infrared+images+with+isolated+and+develop-model+transfer+deep+learning&rft.jtitle=Solar+energy&rft.au=Akram%2C+M.+Waqar&rft.au=Li%2C+Guiqiang&rft.au=Jin%2C+Yi&rft.au=Chen%2C+Xiao&rft.date=2020-03-01&rft.pub=Elsevier+Ltd&rft.issn=0038-092X&rft.eissn=1471-1257&rft.volume=198&rft.spage=175&rft.epage=186&rft_id=info:doi/10.1016%2Fj.solener.2020.01.055&rft.externalDocID=S0038092X20300621
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0038-092X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0038-092X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0038-092X&client=summon