{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T21:34:20Z","timestamp":1781300060630,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T00:00:00Z","timestamp":1616544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the last few decades, photovoltaics have contributed deeply to electric power networks due to their economic and technical benefits. Typically, photovoltaic systems are widely used and implemented in many fields like electric vehicles, homes, and satellites. One of the biggest problems that face the relatability and stability of the electrical power system is the loss of one of the photovoltaic modules. In other words, fault detection methods designed for photovoltaic systems are required to not only diagnose but also clear such undesirable faults to improve the reliability and efficiency of solar farms. Accordingly, the loss of any module leads to a decrease in the efficiency of the overall system. To avoid this issue, this paper proposes an optimum solution for fault finding, tracking, and clearing in an effective manner. Specifically, this proposed approach is done by developing one of the most promising techniques of artificial intelligence called the adaptive neuro-fuzzy inference system. The proposed fault detection approach is based on associating the actual measured values of current and voltage with respect to the trained historical values for this parameter while considering the ambient changes in conditions including irradiation and temperature. Two adaptive neuro-fuzzy inference system-based controllers are proposed: (1) the first one is utilized to detect the faulted string and (2) the other one is utilized for detecting the exact faulted group in the photovoltaic array. The utilized model was installed using a configuration of 4 \u00d7 4 photovoltaic arrays that are connected through several switches, besides four ammeters and four voltmeters. This study is implemented using MATLAB\/Simulink and the simulation results are presented to show the validity of the proposed technique. The simulation results demonstrate the innovation of this study while proving the effective and high performance of the proposed adaptive neuro-fuzzy inference system-based approach in fault tracking, detection, clearing, and rearrangement for practical photovoltaic systems.<\/jats:p>","DOI":"10.3390\/s21072269","type":"journal-article","created":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T21:36:51Z","timestamp":1616621811000},"page":"2269","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System"],"prefix":"10.3390","volume":"21","author":[{"given":"Ahmed F.","family":"Bendary","sequence":"first","affiliation":[{"name":"Department of Electrical Power and Machines Engineering, Faculty of Engineering, Helwan University, Cairo 11795, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5903-5257","authenticated-orcid":false,"given":"Almoataz Y.","family":"Abdelaziz","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed M.","family":"Ismail","sequence":"additional","affiliation":[{"name":"Department of Electrical Power and Machines Engineering, Faculty of Engineering, Helwan University, Cairo 11795, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6729-6809","authenticated-orcid":false,"given":"Karar","family":"Mahmoud","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland"},{"name":"Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9979-7333","authenticated-orcid":false,"given":"Matti","family":"Lehtonen","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9782-8813","authenticated-orcid":false,"given":"Mohamed M. F.","family":"Darwish","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland"},{"name":"Department of Electrical Engineering, Shoubra Faculty of Engineering, Benha University, Cairo 11629, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5628","DOI":"10.1109\/JSEN.2020.3037463","article-title":"Photovoltaic Self-Powered Gas Sensing: A Review","volume":"21","author":"Liu","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1109\/TSG.2016.2587244","article-title":"Fault detection for photovoltaic systems based on multi-resolution signal decomposition and fuzzy inference systems","volume":"8","author":"Yi","year":"2016","journal-title":"IEEE Trans. Smart Grid."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ali, M.N., Mahmoud, K., Lehtonen, M., and Darwish, M.M.F. (2021). Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic. Sensors, 21.","DOI":"10.3390\/s21041244"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JPHOTOV.2020.3047199","article-title":"A Review on Semitransparent Solar Cells for Real-Life Applications Based on Dye-Sensitized Technology","volume":"11","author":"Yeoh","year":"2021","journal-title":"IEEE J. Photovolt."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Abbas, A.S., El-Sehiemy, R.A., Abou El-Ela, A., Ali, E.S., Mahmoud, K., Lehtonen, M., and Darwish, M.M.F. (2021). Optimal Harmonic Mitigation in Distribution Systems with Inverter Based Distributed Generation. Appl. Sci., 11.","DOI":"10.3390\/app11020774"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pei, T., and Hao, X. (2019). A Fault Detection Method for Photovoltaic Systems Based on Voltage and Current Observation and Evaluation. Energies, 12.","DOI":"10.3390\/en12091712"},{"key":"ref_7","first-page":"9","article-title":"Hybrid, optimal, intelligent and classical PV MPPT techniques: A review","volume":"7","author":"Bollipo","year":"2021","journal-title":"CSEE J. Power Energy Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bayoumi, A.S., El-Sehiemy, R.A., Mahmoud, K., Lehtonen, M., and Darwish, M.M.F. (2021). Assessment of an Improved Three-Diode against Modified Two-Diode Patterns of MCS Solar Cells Associated with Soft Parameter Estimation Paradigms. Appl. Sci., 11.","DOI":"10.3390\/app11031055"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Alkahtani, M., Wu, Z., Kuka, C.S., Alahammad, M.S., and Ni, K. (2020). A Novel PV array reconfiguration algorithm approach to optimising power generation across non-uniformly aged PV arrays by merely repositioning. J. Multidiscip. Sci. J., 3.","DOI":"10.3390\/j3010005"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"26420","DOI":"10.1109\/ACCESS.2021.3058052","article-title":"An Efficient Fuzzy-Logic Based Variable-Step Incremental Conductance MPPT Method for Grid-Connected PV Systems","volume":"9","author":"Ali","year":"2021","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/j.solener.2020.10.024","article-title":"Site-specific adjustment of a NWP-based photovoltaic production forecast","volume":"211","author":"Lindfors","year":"2020","journal-title":"Solar Energy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.1016\/j.rser.2015.07.165","article-title":"Progress in renewable electricity in Northern Europe towards EU 2020 targets","volume":"52","author":"Cross","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"117884","DOI":"10.1016\/j.energy.2020.117884","article-title":"Replacing hard coal with wind and nuclear power in Finland-impacts on electricity and district heating markets","volume":"203","author":"Khosravi","year":"2020","journal-title":"Energy"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Eltamaly, A.M., and Abdelaziz, A.Y. (2019). Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems, Springer.","DOI":"10.1007\/978-3-030-05578-3"},{"key":"ref_15","unstructured":"Lin, X., Wang, Y., Pedram, M., Kim, J., and Chang, N. (2012, January 5\u20138). Designing Fault Tolerant Photovoltaic Systems. Proceedings of the International Conference on Computer-Aided Design (ICCAD) 2012, San Jose, CA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Alam, M.K., Khan, F.H., Johnson, J., and Flicker, J. (2013, January 23\u201326). PV faults: Overview, modeling, prevention and detection techniques. Proceedings of the 2013 IEEE 14th Workshop on Control and Modeling for Power Electronics (COMPEL), Salt Lake City, UT, USA.","DOI":"10.1109\/COMPEL.2013.6626400"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Braun, H., Buddha, S.T., Krishnan, V., Spanias, A., Tepedelenlioglu, C., Yeider, T., and Takehara, T. (2012, January 25\u201330). Signal processing for fault detection in photovoltaic arrays. Proceedings of the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan.","DOI":"10.1109\/ICASSP.2012.6288220"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/TSTE.2015.2421447","article-title":"Online Fault Detection in PV Systems","volume":"6","author":"Platon","year":"2015","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_19","unstructured":"Abbas, Y.M., Anis, W.R., and Hafez, I.M. (2016). Automatic Supervision and Fault Detection in PV System by Wireless Sensors With Interfacing By Labview Program. Int. J. Sci. Technol. Res., 5, Available online: https:\/\/www.ijstr.org\/paper-references.php?ref=IJSTR-1216-15833."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.solener.2017.04.043","article-title":"Statistical fault detection in photovoltaic systems","volume":"150","author":"Garoudja","year":"2017","journal-title":"Sol. Energy"},{"key":"ref_21","first-page":"9","article-title":"Thermal Fault Detection System for PV Solar Modules","volume":"6","author":"Uzun","year":"2017","journal-title":"Electr. Electron. Eng. Int. Journal (ELELIJ)"},{"key":"ref_22","unstructured":"Silva, M.F.A.D. (2014). Analysis of New Indicators for Fault Detection in Grid Connected PV Systems for BIPV Applications. [Ph.D. Thesis, Univeridade de Lisboa]."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Braun, H. (2012). Signal Processing and Robust Statistics for Fault Detection in Photovoltaic Arrays. [Ph.D. Thesis, Arizona State University].","DOI":"10.1109\/ICASSP.2012.6288220"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1016\/j.enconman.2010.02.025","article-title":"Automatic supervision and fault detection of PV systems based on power losses analysis","volume":"51","author":"Chouder","year":"2010","journal-title":"Energy Conver. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"225872","DOI":"10.1109\/ACCESS.2020.3045073","article-title":"Fast Corona Discharge Assessment Using FDM integrated With Full Multigrid Method in HVDC Transmission Lines Considering Wind Impact","volume":"8","author":"Abouelatta","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ali, E.S., El-Sehiemy, R.A., Abou El-Ela, A.A., Mahmoud, K., Lehtonen, M., and Darwish, M.M.F. (2021). An Effective Bi-Stage Method for Renewable Energy Sources Integration into Unbalanced Distribution Systems Considering Uncertainty. Processes, 9.","DOI":"10.3390\/pr9030471"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"11911","DOI":"10.1109\/ACCESS.2021.3051807","article-title":"An Improved Neural Network Algorithm to Efficiently Track Various Trajectories of Robot Manipulator Arms","volume":"9","author":"Elsisi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Abaza, A., El-Sehiemy, R.A., Mahmoud, K., Lehtonen, M., and Darwish, M.M.F. (2021). Optimal Estimation of Proton Exchange Membrane Fuel Cells Parameter Based on Coyote Optimization Algorithm. Appl. Sci., 11.","DOI":"10.3390\/app11052052"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Elsisi, M., Mahmoud, K., Lehtonen, M., and Darwish, M.M.F. (2021). Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters. Sensors, 21.","DOI":"10.3390\/s21020487"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Elsisi, M., Tran, M.-Q., Mahmoud, K., Lehtonen, M., and Darwish, M.M.F. (2021). Deep Learning-Based Industry 4.0 and Internet of Things Towards Effective Energy Management for Smart Buildings. Sensors, 21.","DOI":"10.3390\/s21041038"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"30817","DOI":"10.1109\/ACCESS.2021.3060288","article-title":"Enhancing Diagnostic Accuracy of Transformer Faults Using Teaching-Learning-Based Optimization","volume":"9","author":"Ghoneim","year":"2021","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ward, S.A., El-Faraskoury, A.A., Badawi, M., Ibrahim, S.A., Mahmoud, K., Lehtonen, M., and Darwish, M.M.F. (2021). Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors. Sensors, 21.","DOI":"10.3390\/s21062223"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1016\/j.pecs.2008.01.001","article-title":"Artificial intelligence techniques for photovoltaic applications: A review","volume":"34","author":"Mellit","year":"2008","journal-title":"Prog. Energy Combust. Sci."},{"key":"ref_34","unstructured":"Platon, R., Pelland, S., and Poissant, Y. (2012, January 18\u201320). Modelling the power production of a photovoltaic system: Comparison of sugeno-type fuzzy logic and PVSAT-2models. Proceedings of the EuroSun2012, ISES-Europe Solar Conference, Rijeka, Croatia."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.renene.2010.06.028","article-title":"ANFIS-based modelling for photovoltaic power supply system: A case study","volume":"36","author":"Mellit","year":"2011","journal-title":"Renew. Energy"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Schirone, L., Califano, F.P., Moschella, U., and Rocca, U. (1994, January 5-9). Fault finding in a 1 MW photovoltaic plant by reflectometry. Proceedings of the 1994 IEEE 1st World Conference on Photovoltaic Energy Conversion-WCPEC (A Joint Conference of PVSC, PVSEC and PSEC), Waikoloa, HI, USA.","DOI":"10.1109\/WCPEC.1994.520093"},{"key":"ref_37","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."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"37894","DOI":"10.1109\/ACCESS.2021.3063053","article-title":"Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems against Wind Speed Fluctuations","volume":"9","author":"Elsisi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_39","unstructured":"Lin, C.T., and Lee, C.G. (1996). Neural Fuzzy Systems, PTR Prentice Hall."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/0165-0114(91)90110-C","article-title":"Successive identification of a fuzzy model and its applications to prediction of a complex system","volume":"42","author":"Sugeno","year":"1991","journal-title":"Fuzzy Sets Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2269\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:40:14Z","timestamp":1760161214000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2269"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,24]]},"references-count":40,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21072269"],"URL":"https:\/\/doi.org\/10.3390\/s21072269","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,24]]}}}