{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:24:07Z","timestamp":1778693047998,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T00:00:00Z","timestamp":1674345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004054","name":"Institutional Fund Projects, King Abdulaziz University","doi-asserted-by":"publisher","award":["IFPIP-1059-829-1442"],"award-info":[{"award-number":["IFPIP-1059-829-1442"]}],"id":[{"id":"10.13039\/501100004054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The use of artificial intelligence to automate PV module fault detection, diagnosis, and classification processes has gained interest for PV solar plants maintenance planning and reduction in expensive inspection and shutdown periods. The present article reports on the development of an adaptive neuro-fuzzy inference system (ANFIS) for PV fault classification based on statistical and mathematical features extracted from outdoor infrared thermography (IRT) and I-V measurements of thin-film PV modules. The selection of the membership function is shown to be essential to obtain a high classifier performance. Principal components analysis (PCA) is used to reduce the dimensions to speed up the classification process. For each type of fault, effective features that are highly correlated to the PV module\u2019s operating power ratio are identified. Evaluation of the proposed methodology, based on datasets gathered from a typical PV plant, reveals that features extraction methods based on mathematical parameters and I-V measurements provide a 100% classification accuracy. On the other hand, features extraction based on statistical factors provides 83.33% accuracy. A novel technique is proposed for developing a correlation matrix between the PV operating power ratio and the effective features extracted online from infrared thermal images. This eliminates the need for offline I-V measurements to estimate the operating power ratio of PV modules.<\/jats:p>","DOI":"10.3390\/s23031280","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T01:36:26Z","timestamp":1674437786000},"page":"1280","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Fault Detection and Classification of CIGS Thin-Film PV Modules Using an Adaptive Neuro-Fuzzy Inference Scheme"],"prefix":"10.3390","volume":"23","author":[{"given":"Reham A.","family":"Eltuhamy","sequence":"first","affiliation":[{"name":"Mechanical Engineering Department, Faculty of Engineering, Helwan University, Cairo 11795, Egypt"},{"name":"Mechanical Engineering Department, Ahram Canadian University, Cairo 12451, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5745-4538","authenticated-orcid":false,"given":"Mohamed","family":"Rady","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, Faculty of Engineering at Rabigh, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2009-8335","authenticated-orcid":false,"given":"Eydhah","family":"Almatrafi","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, Faculty of Engineering at Rabigh, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7873-0586","authenticated-orcid":false,"given":"Haitham A.","family":"Mahmoud","sequence":"additional","affiliation":[{"name":"Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khaled H.","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Electrical Power Department, Faculty of Engineering, Fayoum University, El-Fayoum 63514, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,22]]},"reference":[{"key":"ref_1","unstructured":"Feldman, R., Wu, K., and Margolis, R. (2022, April 12). Solar Industry Update, Available online: https:\/\/www.nrel.gov\/docs\/fy21osti\/80427.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1002\/ese3.255","article-title":"Fault diagnosis of photovoltaic modules","volume":"7","author":"Haque","year":"2019","journal-title":"Energy Sci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"17","DOI":"10.15866\/irecon.v9i1.19350","article-title":"Failure Mode and Effects Analysis of CIGS Thin Film PV Modules Using Thermography Analysis and IV Measurements","volume":"9","author":"Ibrahim","year":"2021","journal-title":"Int. J. Energy Convers. (IRECON)"},{"key":"ref_4","unstructured":"K\u00f6ntges, M., Kurtz, S., Packard, C., Jahn, U., Berger, K.A., Kato, K., Friesen, T., Liu, H., and Iseghem, M.V. (2014). Review of Failures of Photovoltaic Modules, Report IEA-PVPS T13-01:2014."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.solmat.2012.07.011","article-title":"Reliability of IR-imaging of PV-plants under operating conditions","volume":"107","author":"Buerhop","year":"2012","journal-title":"Sol. Energy Mater. Sol. Cells"},{"key":"ref_6","first-page":"197","article-title":"Fault Analysis of Solar PV Array Based on Infrared Image","volume":"31","author":"Wang","year":"2010","journal-title":"Acta Energ. Sol. Sin."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1109\/TEC.2007.914308","article-title":"MATLAB-Based Modeling to Study the Effects of Partial Shading on PV Array Characteristics","volume":"23","author":"Patel","year":"2008","journal-title":"IEEE Trans. Energy Convers."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kumar, A., Pachauri, R.K., and Chauhan, Y.K. (2016, January 4\u20136). Experimental analysis of SP\/TCT PV array configurations under partial shading conditions. Proceedings of the 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, India.","DOI":"10.1109\/ICPEICES.2016.7853403"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s40095-017-0252-6","article-title":"Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods","volume":"8","author":"Bechouat","year":"2017","journal-title":"Int. J. Energy Environ. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"02004","DOI":"10.21272\/jnep.9(2).02004","article-title":"Analysis on Solar Panel Crack Detection Using Optimization Techniques","volume":"9","author":"Lydia","year":"2017","journal-title":"J. Nano Electron. Phys."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.asoc.2017.05.017","article-title":"A new MPPT controller based on the Ant colony optimization algorithm for Photovoltaic systems under partial shading conditions","volume":"58","author":"Titri","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_12","first-page":"967","article-title":"Fault Detection of Solar PV system using SVM and Thermal Image Processing","volume":"10","author":"Natarajan","year":"2020","journal-title":"Int. J. Renew. Energy Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.solener.2018.07.038","article-title":"Modeling of PV system based on experimental data for fault detection using kNN method","volume":"173","author":"Madeti","year":"2018","journal-title":"Sol. Energy"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shin, J.H., and Kim, J.O. (2020). On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant. Energies, 13.","DOI":"10.3390\/en13174584"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Yang, L., Lehman, B., de Palma, J.-F., Mosesian, J., and Lyons, R. (2012, January 5\u20139). Decision tree-based fault detection and classification in solar photovoltaic arrays. Proceedings of the Twenty-Seventh Annual IEEE Applied Power Electronics Conference and Exposition (APEC), Orlando, FL, USA.","DOI":"10.1109\/APEC.2012.6165803"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sun, J., Sun, F., Fan, J., and Liang, Y. (2017). Fault Diagnosis Model of Photovoltaic Array Based on Least Squares Support Vector Machine in Bayesian Framework. Appl. Sci., 7.","DOI":"10.3390\/app7111199"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"893","DOI":"10.5194\/isprs-archives-XLII-2-893-2018","article-title":"Deep convolutional neural network for automatic detection of damaged photovoltaic cells","volume":"XLII-2","author":"Pierdicca","year":"2018","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.infrared.2017.04.015","article-title":"Scheme for predictive fault diagnosis in photo-voltaic modules using thermal imaging","volume":"83","author":"Jaffery","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.renene.2018.05.008","article-title":"Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification","volume":"127","author":"Belaout","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1007\/978-3-319-26181-2_10","article-title":"Online Monitoring and Fault Diagnosis of PV Array Based on BP Neural Network Optimized by Genetic Algorithm","volume":"9426","author":"Lin","year":"2015","journal-title":"Multi-Discip. Trends Artif. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kurukuru, V.S.B., Haque, A., Khan, M.A., and Tripathy, A.K. (2019, January 3\u20134). Fault classification for Photovoltaic Modules Using Thermography and Machine Learning Techniques. Proceedings of the International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia.","DOI":"10.1109\/ICCISci.2019.8716442"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1002\/pip.3191","article-title":"Photovoltaic defect classification through thermal infrared imaging using a machine learning approach","volume":"28","author":"Dunderdale","year":"2020","journal-title":"Prog. Photovolt. Res. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Niazi, K., Akhtar, W., Khan, H.A., Sohaib, S., and Nasir, A.K. (2018, January 10\u201315). Binary Classification of Defective Solar PV Modules Using Thermography. Proceedings of the 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC), Waikoloa, HI, USA.","DOI":"10.1109\/PVSC.2018.8548138"},{"key":"ref_24","unstructured":"Leotta, G., Pugliatti, P., Di Stefano, A., Aleo, F., and Bizzarri, F. (2015, January 14\u201318). Post processing technique for thermographic images provided by drone inspections. Proceedings of the 31st European Photovoltaic Solar Energy Conference and Exhibition (EU PVSEC), Hamburg, Germany."},{"key":"ref_25","unstructured":"Rasch, R., Behrens, G., Hamelmann, F., Hantelmann, S., Dreimann, R., and Weicht, J. (2015, January 14\u201318). Automated Thermal Imaging for Fault Detection on PV-Systems. Proceedings of the 31st European Photovoltaic Solar Energy Conference and Exhibition (EU PVSEC), Hamburg, Germany."},{"key":"ref_26","first-page":"101545","article-title":"Remote anomaly detection and classification of solar photovoltaic modules based on deep neural network","volume":"48","author":"Le","year":"2021","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.solener.2018.12.048","article-title":"PV shading fault detection and classification based on I-V curve using principal component analysis: Application to isolated PV system","volume":"179","author":"Fadhel","year":"2018","journal-title":"Sol. Energy"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Samara, S., and Natsheh, E. (2020). Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems. Sustainability, 12.","DOI":"10.3390\/su12052011"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"102235","DOI":"10.1109\/ACCESS.2020.2996969","article-title":"Modeling and Fault Categorization in Thin-Film and Crystalline PV Arrays through Multilayer Neural Network Algorithm","volume":"8","author":"Sindi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"41889","DOI":"10.1109\/ACCESS.2020.2977116","article-title":"A Novel Convolutional Neural Network Based Approach for Fault Classification in Photovoltaic Arrays","volume":"8","author":"Aziz","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"70919","DOI":"10.1109\/ACCESS.2019.2919337","article-title":"Newly Designed Fault Diagnostic Method for Solar Photovoltaic Generation System Based on IV-Curve Measurement","volume":"7","author":"Huang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1016\/j.egypro.2017.03.462","article-title":"An Intelligent Fault Diagnosis Approach for PV Array Based on SA-RBF Kernel Extreme Learning Machine","volume":"105","author":"Wu","year":"2017","journal-title":"Energy Procedia"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.simpat.2016.05.005","article-title":"Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules","volume":"67","author":"Mekki","year":"2016","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.egypro.2014.12.405","article-title":"Neuro-Fuzzy Fault Detection Method for Photovoltaic Systems","volume":"62","author":"Bonsignore","year":"2014","journal-title":"Energy Procedia"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lazzaretti, A.E., Da Costa, C.H., Rodrigues, M.P., Yamada, G.D., Lexinoski, G., Moritz, G.L., Oroski, E., De Goes, R.E., Linhares, R.R., and Stadzisz, P.C. (2020). A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants. Sensors, 20.","DOI":"10.3390\/s20174688"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1016\/j.renene.2020.04.023","article-title":"Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters","volume":"155","author":"Hussain","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1109\/TDMR.2019.2944793","article-title":"Photovoltaic Hot-Spots Fault Detection Algorithm Using Fuzzy Systems","volume":"19","author":"Dhimish","year":"2019","journal-title":"IEEE Trans. Device Mater. Reliab."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"311","DOI":"10.18280\/i2m.190501","article-title":"Novel features extraction for fault detection using thermography characteristics and IV measurements of CIGS thin-film module","volume":"19","author":"Eltuhamy","year":"2020","journal-title":"Instrum. Mes. M\u00e9trologie"},{"key":"#cr-split#-ref_39.1","unstructured":"International Electrotechnical Commission (2017). Photovoltaic"},{"key":"#cr-split#-ref_39.2","unstructured":"(PV) Systems-Requirements for Testing, Documentation and Maintenance-Part 3: Photovoltaic Modules and Plants-Outdoor Infrared Thermography, International Electrotechnical Commission (IEC). IEC TS 62446-3: 2007."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/21.256541","article-title":"ANFIS: Adaptive-network-based fuzzy inference system","volume":"23","author":"Jang","year":"1993","journal-title":"IEEE Trans. Syst. Man Cybern."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1280\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:13:45Z","timestamp":1760120025000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1280"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,22]]},"references-count":41,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031280"],"URL":"https:\/\/doi.org\/10.3390\/s23031280","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,22]]}}}