{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:00:41Z","timestamp":1772910041898,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,25]],"date-time":"2023-02-25T00:00:00Z","timestamp":1677283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U1909204"],"award-info":[{"award-number":["U1909204"]}]},{"name":"National Natural Science Foundation of China","award":["U19B2029"],"award-info":[{"award-number":["U19B2029"]}]},{"name":"National Natural Science Foundation of China","award":["2020ZDLGY10-04"],"award-info":[{"award-number":["2020ZDLGY10-04"]}]},{"name":"Key Industrial Innovation Chain Project of Shaanxi Province Key R&amp;D Program of China","award":["U1909204"],"award-info":[{"award-number":["U1909204"]}]},{"name":"Key Industrial Innovation Chain Project of Shaanxi Province Key R&amp;D Program of China","award":["U19B2029"],"award-info":[{"award-number":["U19B2029"]}]},{"name":"Key Industrial Innovation Chain Project of Shaanxi Province Key R&amp;D Program of China","award":["2020ZDLGY10-04"],"award-info":[{"award-number":["2020ZDLGY10-04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The widespread adoption of green energy resources worldwide, such as photovoltaic (PV) systems to generate green and renewable power, has prompted safety and reliability concerns. One of these concerns is fault diagnostics, which is needed to manage the reliability and output of PV systems. Severe PV faults make detecting faults challenging because of drastic weather circumstances. This research article presents a novel deep stack-based ensemble learning (DSEL) approach for diagnosing PV array faults. The DSEL approach compromises three deep-learning models, namely, deep neural network, long short-term memory, and Bi-directional long short-term memory, as base learners for diagnosing PV faults. To better analyze PV arrays, we use multinomial logistic regression as a meta-learner to combine the predictions of base learners. This study considers open circuits, short circuits, partial shading, bridge, degradation faults, and incorporation of the MPPT algorithm. The DSEL algorithm offers reliable, precise, and accurate PV-fault diagnostics for noiseless and noisy data. The proposed DSEL approach is quantitatively examined and compared to eight prior machine-learning and deep-learning-based PV-fault classification methodologies by using a simulated dataset. The findings show that the proposed approach outperforms other techniques, achieving 98.62% accuracy for fault detection with noiseless data and 94.87% accuracy with noisy data. The study revealed that the DSEL algorithm retains a strong generalization potential for detecting PV faults while enhancing prediction accuracy. Hence, the proposed DSEL algorithm detects and categorizes PV array faults more efficiently, reliably, and accurately.<\/jats:p>","DOI":"10.3390\/rs15051277","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T01:59:10Z","timestamp":1677463150000},"page":"1277","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A Novel Deep Stack-Based Ensemble Learning Approach for Fault Detection and Classification in Photovoltaic Arrays"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7967-1004","authenticated-orcid":false,"given":"Ehtisham","family":"Lodhi","sequence":"first","affiliation":[{"name":"The SKL for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9185-3989","authenticated-orcid":false,"given":"Fei-Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"The SKL for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"The Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4303-5559","authenticated-orcid":false,"given":"Gang","family":"Xiong","sequence":"additional","affiliation":[{"name":"The SKL for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"The Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3844-5625","authenticated-orcid":false,"given":"Lingjian","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Precision Instrument Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3700-928X","authenticated-orcid":false,"given":"Tariku Sinshaw","family":"Tamir","sequence":"additional","affiliation":[{"name":"The SKL for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5649-7381","authenticated-orcid":false,"given":"Waheed Ur","family":"Rehman","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, University of Chakwal, Chakwal 48800, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9435-1431","authenticated-orcid":false,"given":"M. Adil","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer and Technology, Chang\u2019an University, Xi\u2019an 710062, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, L., Lodhi, E., Yang, P., Qiu, H., Rehman, W.U., Lodhi, Z., Tamir, T.S., and Khan, M.A. (2022). Adaptive Local Mean Decomposition and Multiscale-Fuzzy Entropy-Based Algorithms for the Detection of DC Series Arc Faults in PV Systems. Energies, 15.","DOI":"10.3390\/en15103608"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"112160","DOI":"10.1016\/j.rser.2022.112160","article-title":"Review of degradation and failure phenomena in photovoltaic modules","volume":"159","author":"Aghaei","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hojabri, M., Kellerhals, S., Upadhyay, G., and Bowler, B. (2022). IoT-Based PV Array Fault Detection and Classification Using Embedded Supervised Learning Methods. Energies, 15.","DOI":"10.3390\/en15062097"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.esr.2019.01.006","article-title":"The role of renewable energy in the global energy transformation","volume":"24","author":"Gielen","year":"2019","journal-title":"Energy Strategy Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.renene.2022.01.056","article-title":"Experimental assessment of long-term performance degradation for a PV power plant operating in a desert maritime climate","volume":"187","author":"Daher","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1049\/el.2018.6510","article-title":"Fault detection for PV systems using Teager\u2013Kaiser energy operator","volume":"54","author":"Khoshnami","year":"2018","journal-title":"Electron. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.rser.2018.03.065","article-title":"Potential of solar energy in developing countries for reducing energy-related emissions","volume":"90","author":"Shahsavari","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lodhi, E., Yang, P., Wang, L., Lodhi, Z., Khan, M.A., Muhammad, S., and Tamir, T.S. (2021). Modelling and Experimental Characteristics of Photovoltaic Modules in Typical Days at an Actual Photovoltaic Power Station, Proceedings of the IEEE 4th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), Shenyang, China, 19\u201321 November 2021, IEEE.","DOI":"10.1109\/AUTEEE52864.2021.9668658"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1007\/s42452-020-03315-8","article-title":"Modeling and design of the improved D-STATCOM control for power distribution grid","volume":"2","author":"Kerrouche","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lodhi, E., Jing, S., Lodhi, Z., Shafqat, R.N., and Ali, M. (2017). Rapid and Efficient MPPT Technique with Competency of High Accurate Power Tracking for PV System, Proceedings of the 2017 4th International Conference on Information Science and Control Engineering (ICISCE), Changsha, China, 21\u201323 July 2017, IEEE.","DOI":"10.1109\/ICISCE.2017.229"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1016\/j.solener.2009.08.004","article-title":"A simple model of PV system performance and its use in fault detection","volume":"84","author":"Firth","year":"2010","journal-title":"Sol. Energy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3784","DOI":"10.1109\/TIE.2012.2205355","article-title":"Line\u2013Line Fault Analysis and Protection Challenges in Solar Photovoltaic Arrays","volume":"60","author":"Zhao","year":"2012","journal-title":"IEEE Trans. Ind. Electron."},{"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":"Lodhi, E., Lina, W., Pu, Y., Javed, M.Y., Lodhi, Z., Zhijie, J., and Javed, U. (2020). Performance Evaluation of Faults in a Photovoltaic Array Based on VI and VP Characteristic Curve, Proceedings of the 2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Phuket, Thailand, 28\u201329 February 2020, IEEE.","DOI":"10.1109\/ICMTMA50254.2020.00027"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"8646","DOI":"10.1109\/TPEL.2018.2884292","article-title":"An MPPT-Based Sensorless Line\u2013Line and Line\u2013Ground Fault Detection Technique for PV Systems","volume":"34","author":"Pillai","year":"2018","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPHOTOV.2017.2770159","article-title":"Online Fault Detection and Diagnosis in Photovoltaic Systems Using Wavelet Packets","volume":"8","author":"Kumar","year":"2018","journal-title":"IEEE J. Photovolt."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Schirone, L., Califano, F.P., Moschella, U., and Rocca, U. (1994, January 5\u20139). Fault finding in a 1 MW photovoltaic plant by refectometry. 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_18","doi-asserted-by":"crossref","first-page":"7046","DOI":"10.1109\/TPEL.2017.2755592","article-title":"An irradiance independent, robust ground-fault detection scheme for PV arrays based on spread spectrum time-domain refectometry (SSTDR)","volume":"33","author":"Roy","year":"2018","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"229","DOI":"10.3103\/S0003701X17030069","article-title":"Fault detection in PV systems","volume":"53","author":"Jenitha","year":"2017","journal-title":"Appl. Sol. Energy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1966","DOI":"10.1049\/iet-rpg.2018.5220","article-title":"Sample entropy-based fault detection for photovoltaic arrays","volume":"12","author":"Khoshnami","year":"2018","journal-title":"IET Renew. Power Gener."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1109\/TSG.2015.2478855","article-title":"Photovoltaic energy conversion system fault detection using fractional-order color relation classier in microdistribution systems","volume":"8","author":"Kuo","year":"2017","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1109\/JPHOTOV.2016.2581478","article-title":"A method to detect photovoltaic array faults and partial shading in PV systems","volume":"6","author":"Hariharan","year":"2016","journal-title":"IEEE J. Photovolt."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8546","DOI":"10.1109\/TIE.2017.2703681","article-title":"Line-to-Line Fault Detection for Photovoltaic Arrays Based on Multiresolution Signal Decomposition and Two-Stage Support Vector Machine","volume":"64","author":"Yi","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1016\/j.apenergy.2017.05.034","article-title":"Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I\u2013V characteristics","volume":"204","author":"Chen","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5300","DOI":"10.1109\/TII.2019.2908992","article-title":"Fault Diagnosis in Photovoltaic Arrays Using GBSSL Method and Proposing a Fault Correction System","volume":"16","author":"Momeni","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"50287","DOI":"10.1109\/ACCESS.2019.2911250","article-title":"Intelligent Real-Time Photovoltaic Panel Monitoring System Using Artificial Neural Networks","volume":"7","author":"Samara","year":"2019","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dairi, A., Harrou, F., Sun, Y., and Khadraoui, S. (2020). Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach. Appl. Sci., 10.","DOI":"10.3390\/app10238400"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nitisanon, S., and Hoonchareon, N. (2017, January 16\u201320). Solar Power Forecast with Weather Classification Using Self-Organized Map. Proceedings of the 2017 IEEE Power & Energy Society General Meeting, Chicago, IL, USA.","DOI":"10.1109\/PESGM.2017.8274548"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"De, V., Teo, T.T., Woo, W.L., and Logenthiran, T. (2018, January 22\u201325). Photovoltaic power forecasting using LSTM on limited dataset. Proceedings of the 2018 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), Singapore.","DOI":"10.1109\/ISGT-Asia.2018.8467934"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1109\/TSTE.2015.2425791","article-title":"Modeling and Health Monitoring of DC Side of Photovoltaic Array","volume":"6","author":"Akram","year":"2015","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ahmad, S., Hasan, N., Kurukuru, V.B., Khan, M.A., and Haque, A. (2018, January 13\u201315). Fault classification for single phase photovoltaic systems using machine learning techniques. Proceedings of the 2018 8th IEEE India International Conference on Power Electronics (IICPE), Jaipur, India.","DOI":"10.1109\/IICPE.2018.8709463"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"111793","DOI":"10.1016\/j.enconman.2019.111793","article-title":"Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions","volume":"198","author":"Chen","year":"2019","journal-title":"Energy Convers. Manag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1109\/JRFID.2022.3212310","article-title":"An AdaBoost Ensemble Model for Fault Detection and Classification in Photovoltaic Arrays","volume":"6","author":"Lodhi","year":"2022","journal-title":"IEEE J. Radio Freq. Identif."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"6960328","DOI":"10.1155\/2020\/6960328","article-title":"An Intelligent Fault Detection Model for Fault Detection in Photovoltaic Systems","volume":"2020","author":"Basnet","year":"2020","journal-title":"J. Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.ymssp.2017.03.051","article-title":"Fault diagnosis for rotary machinery with selective ensemble neural networks","volume":"113","author":"Wang","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.energy.2018.08.207","article-title":"Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression","volume":"164","author":"Ahmad","year":"2018","journal-title":"Energy"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4624","DOI":"10.1109\/TII.2018.2882598","article-title":"An Ensemble Framework for Day-Ahead Forecast of PV Output Power in Smart Grids","volume":"15","author":"Raza","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhou, Z.-H. (2012). Ensemble Methods: Foundations and Algorithms, CRC Press.","DOI":"10.1201\/b12207"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"De Guia, J.D., Concepcion, R.S., Calinao, H.A., Lauguico, S.C., Dadios, E.P., and Vicerra, R.R.P. (2020, January 6\u20138). Application of Ensemble Learning with Mean Shift Clustering for Output Profile Classification and Anomaly Detection in Energy Production of Grid-Tied Photovoltaic System. Proceedings of the 2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE), Yogyakarta, Indonesia.","DOI":"10.1109\/ICITEE49829.2020.9271699"},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"120463","DOI":"10.1016\/j.energy.2021.120463","article-title":"A supervised ensemble learning method for fault diagnosis in photovoltaic strings","volume":"227","author":"Kapucu","year":"2021","journal-title":"Energy"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lodhi, E., Wang, F.-Y., Xiong, G., Mallah, G.A., Javed, M.Y., Tamir, T.S., and Gao, D.W. (2021). A Dragonfly Optimization Algorithm for Extracting Maximum Power of Grid-Interfaced PV Systems. Sustainability, 13.","DOI":"10.3390\/su131910778"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"73992","DOI":"10.1109\/ACCESS.2020.2988550","article-title":"Sentiment Classification Using a Single-Layered BiLSTM Model","volume":"8","author":"Hameed","year":"2020","journal-title":"IEEE Access"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1277\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:42:30Z","timestamp":1760121750000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1277"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,25]]},"references-count":44,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051277"],"URL":"https:\/\/doi.org\/10.3390\/rs15051277","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,25]]}}}