{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T18:26:46Z","timestamp":1775845606315,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T00:00:00Z","timestamp":1680220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Grants Council of the Hong Kong Special Administrative Region, China","award":["UGC\/FDS13\/E01\/21"],"award-info":[{"award-number":["UGC\/FDS13\/E01\/21"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Photovoltaic (PV) panels have been widely used as one of the solutions for green energy sources. Performance monitoring, fault diagnosis, and Control of Operation at Maximum Power Point (MPP) of PV panels became one of the popular research topics in the past. Model parameters could reflect the health conditions of a PV panel, and model parameter estimation can be applied to PV panel fault diagnosis. In this paper, we will propose a new algorithm for PV panel model parameters estimation by using a Neural Network (ANN) with a Numerical Current Prediction (NCP) layer. Output voltage and current signals (VI) after load perturbation are observed. An ANN is trained to estimate the PV panel model parameters, which is then fined tuned by the NCP to improve the accuracy to about 6%. During the testing stage, VI signals are input into the proposed ANN-NCP system. PV panel model parameters can then be estimated by the proposed algorithms, and the estimated model parameters can be then used for fault detection, health monitoring, and tracking operating points for MPP conditions.<\/jats:p>","DOI":"10.3390\/s23073657","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T09:12:59Z","timestamp":1680253979000},"page":"3657","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["PV Panel Model Parameter Estimation by Using Neural Network"],"prefix":"10.3390","volume":"23","author":[{"given":"Wai Lun","family":"Lo","sequence":"first","affiliation":[{"name":"Department of Computer Science, Hong Kong Chu Hai College, 80 Castle Peak Road, Castle Peak Bay, Tuen Mun, N.T. Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henry Shu Hung","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, City University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7699-1937","authenticated-orcid":false,"given":"Richard Tai Chiu","family":"Hsung","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Chu Hai College, 80 Castle Peak Road, Castle Peak Bay, Tuen Mun, N.T. Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2246-7552","authenticated-orcid":false,"given":"Hong","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6151-0245","authenticated-orcid":false,"given":"Tak Wai","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Chu Hai College, 80 Castle Peak Road, Castle Peak Bay, Tuen Mun, N.T. Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1764","DOI":"10.1109\/TPEL.2012.2213270","article-title":"Design, analysis, and implementation of solar power optimizer for DC distribution system","volume":"28","author":"Chen","year":"2013","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1852","DOI":"10.1109\/TPEL.2012.2210737","article-title":"Development and operational control of two-string maximum power point trackers in dc distribution systems","volume":"28","author":"Chang","year":"2013","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2936","DOI":"10.1109\/TPEL.2012.2226476","article-title":"Characterization of power optimizer potential to increase energy capture in photovoltaic systems operating under nonuniform conditions","volume":"28","author":"MacAlpine","year":"2013","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5784","DOI":"10.1109\/TPEL.2013.2260562","article-title":"Technical considerations on power conversion for electric and plug-in hybrid electric vehicle battery charging in photovoltaic installations","volume":"28","author":"Carli","year":"2013","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fahim, S.R., Hasanien, H.M., Turky, R.A., Aleem, S.H.E.A., and \u0106alasan, M. (2022). A Comprehensive Review of Photovoltaic Modules Models and Algorithms Used in Parameter Extraction. Energies, 15.","DOI":"10.3390\/en15238941"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Henry, C., Poudel, S., Lee, S.-W., and Jeong, H. (2020). Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera. Appl. Sci., 10.","DOI":"10.3390\/app10113802"},{"key":"ref_7","unstructured":"(2009). Photovoltaic Devices-Procedures for Temperature and Irradiance Corrections to Measured I-V Characteristics (Standard No. IEC Standard 60891)."},{"key":"ref_8","unstructured":"Kuntz, G., and Wagner, A. (2004, January 7\u201311). Internal series resistance determined of only one IV-curve under illumination. Proceedings of the 19th European Photovoltaic Solar Energy Conference, Paris, France."},{"key":"ref_9","unstructured":"Kaminski, A., Marchand, J., Fave, A., and Laugier, A. (October, January 29). New method of parameters extraction from dark I-V curve. Proceedings of the Photovoltaic Specialists Conference, Anaheim, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1109\/TPEL.2008.2009056","article-title":"A system design approach for unattended solar energy harvesting supply","volume":"24","author":"Kimball","year":"2009","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6271","DOI":"10.1109\/TPEL.2014.2332754","article-title":"A hybrid power control concept for PV inverters with reduced thermal loading","volume":"29","author":"Yang","year":"2014","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_12","unstructured":"Laukamp, H. (2002). Reliability Study of Grid Connected PV Systems-Field Experience and Recommended Design Practice, Report IEA-PVPS T7-08; International Energy Agency. March."},{"key":"ref_13","unstructured":"Sera, D. (2010, January 6\u201310). Series resistance monitoring for photovoltaic modules in the vicinity of MPP. Proceedings of the 25th European Photovoltaic Solar Energy Conference and Exhibition, Valencia, Spain."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sera, D., Teodorescu, R., and Rodriguez, P. (2008, January 10\u201313). Photovoltaic module diagnostics by series resistance monitoring and temperature and rated power estimation. Proceedings of the IEEE 34th Annual Conference of IEEE Industrial Electronics, Orlando, FL, USA.","DOI":"10.1109\/IECON.2008.4758297"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Suskis, P., and Galkin, I. (2013, January 10\u201313). Enhanced photovoltaic panel model for MATLAB-simulink environment considering solar cell junction capacitance. Proceedings of the IEEE 39th Annual Conference of the IEEE Industrial Electronics Society, Vienna, Austria.","DOI":"10.1109\/IECON.2013.6699374"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shekoofa, O., and Taherbaneh, M. (2007, January 14\u201316). Modelling of silicon solar panel by MATLAB\/simulink and evaluating the importance of its parameters in a space application. Proceedings of the 3rd International Conference on Recent Advances in Space Technologies, Istanbul, Turkey.","DOI":"10.1109\/RAST.2007.4284087"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Brito, E.M.D.S., Carlette, L.P., Filho, D.O., Pereira, H.A., and Ribeiro, P.F. (2012, January 5\u20137). Comparison of solar panel models for grid integrations studies. Proceedings of the IEEE\/IAS International Conference on Industry Applications, Fortaleza, Brazil.","DOI":"10.1109\/INDUSCON.2012.6453866"},{"key":"ref_18","unstructured":"Wei, H., and Cong, J. (2011, January 15\u201317). Extracting solar cell model parameters based on chaos particle swarm algorithm. Proceedings of the International Conference on Electric Information and Control Engineering, Wuhan, China."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1109\/TEC.2008.2003206","article-title":"Development of a MATLAB\/Simulink model of a single-phase grid-connected photovoltaic system","volume":"24","author":"Ropp","year":"2009","journal-title":"IEEE Trans. Energy Convers."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1109\/TPEL.2009.2013862","article-title":"Comprehensive approach to modeling and simulation of photovoltaic arrays","volume":"24","author":"Villalva","year":"2009","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2732","DOI":"10.1063\/1.1777380","article-title":"Measurement of solar cell AC parameters using the time domain technique","volume":"75","author":"Deshmukh","year":"2004","journal-title":"Rev. Sci. Instrum."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1109\/TPEL.2005.869779","article-title":"Effect of solar array capacitance on the performance of switching shunt voltage regulator","volume":"21","author":"Kumar","year":"2006","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3975","DOI":"10.1109\/TPEL.2012.2188818","article-title":"Photovoltaic model identification using particle swarm optimization with inverse barrier constraint","volume":"27","author":"Soon","year":"2012","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/TSMCB.2009.2015956","article-title":"Adaptive particle swarm optimization","volume":"39","author":"Zhan","year":"2009","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"key":"ref_25","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.rser.2013.02.011","article-title":"Maximum power point tracking control techniques: State-of-the-art in photovoltaic applications","volume":"23","author":"Bhatnagar","year":"2013","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1109\/TEC.2006.874230","article-title":"Comparison of photovoltaic array maximum power point tracking techniques","volume":"22","author":"Esram","year":"2007","journal-title":"IEEE Trans. Energy Convers."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yeung, R., Chung, H., and Chuang, S. (2014, January 16\u201320). A global MPPT algorithm for PV system under rapidly fluctuating irradiance. Proceedings of the IEEE 29th Annual IEEE Applied Power Electronics Conference and Exposition, Fort Worth, TX, USA.","DOI":"10.1109\/APEC.2014.6803379"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1109\/TPEL.2012.2206830","article-title":"In-site real-time photovoltaic I-V curves and maximum power point estimator","volume":"28","author":"Blanes","year":"2013","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_30","first-page":"3195","article-title":"A deterministic particle swarm optimization maximum power point tracker for photovoltaic system under partial shading condition","volume":"60","author":"Ishaque","year":"2013","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1109\/TAES.2011.5705681","article-title":"Maximum power point tracking of multiple photovoltaic arrays: A PSO approach","volume":"47","author":"Miyatake","year":"2011","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/JPHOTOV.2013.2297513","article-title":"A maximum power point tracking method based on perturb-and-observe combined with particle swarm optimization","volume":"4","author":"Lian","year":"2014","journal-title":"IEEE J. Photovolt."},{"key":"ref_33","unstructured":"Jayakrishnan, K.B., Umashankar, S., Vijayakumar, D., and Kothari, D.P. (2011, January 16\u201318). Perturb and observe MPPT algorithm for solar PV systems-modeling and simulation. Proceedings of the 2011 Annual IEEE India Conference, Hyderabad, India."},{"key":"ref_34","first-page":"213","article-title":"Simulation and Analysis of Perturb and Observe MPPT Algorithm for PV Array Using \u010aUK Converter","volume":"4","author":"Sahu","year":"2014","journal-title":"Adv. Electron. Electr. Eng."},{"key":"ref_35","unstructured":"Putri, R.I., Wibowo, S., and Rif\u2019I, M. (2014, January 14\u201316). Maximum power point tracking for photovoltaic using incremental conductance method. Proceedings of the 2nd International Conference on Sustainable Energy Engineering and Application, ICSEEA 2014, Bandung, Indonesia."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s41601-020-00161-z","article-title":"An improved MPPT control strategy based on incremental conductance algorithm","volume":"5","author":"Shang","year":"2020","journal-title":"Prot. Control Mod. Power Syst."},{"key":"ref_37","first-page":"588","article-title":"Fault Diagnosis of Solar Panels Using Dynamic Current-Voltage Characteristics","volume":"31","author":"Wang","year":"2016","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1016\/j.solener.2011.07.009","article-title":"Artificial neural network modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules","volume":"85","author":"Celik","year":"2011","journal-title":"Sol. Energy"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.solener.2018.10.054","article-title":"Fault diagnosis approach for photovoltaic arrays based on unsupervised sample clustering and probabilistic neural network model","volume":"176","author":"Zhua","year":"2018","journal-title":"Sol. Energy"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1016\/j.solener.2019.01.037","article-title":"Modeling of solar energy systems using artificial neural network: A comprehensive review","volume":"180","author":"Elsheikh","year":"2019","journal-title":"Sol. Energy"},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.micpro.2016.11.003","article-title":"Hardware implementation of an artificial neural network model to predict the energy production of a photovoltaic system","volume":"49","author":"Baptista","year":"2017","journal-title":"Microprocess. Microsyst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Caputo, D., Grimaccia, F., Mussetta, M., and Zich, R.E. (2010, January 18\u201323). Photovoltaic Plants Predictive Model by means of ANN trained by a Hybrid Evolutionary Algorithm. Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain.","DOI":"10.1109\/IJCNN.2010.5596782"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lee, J., and Kim, Y. (2022). Comparative Estimation of Electrical Characteristics of a Photovoltaic Module Using Regression and Artificial Neural Network Models. Electronics, 11.","DOI":"10.3390\/electronics11244228"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez, I., Portalo, J.M., and Calder\u00f3n, A.J. (2021). Configurable IoT Open-Source Hardware and Software I-V Curve Tracer for Photovoltaic Generators. Sensors, 21.","DOI":"10.3390\/s21227650"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.enconman.2017.10.008","article-title":"Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network","volume":"153","author":"Wang","year":"2017","journal-title":"Energy Convers. Manag."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1016\/j.egypro.2017.12.126","article-title":"Forecasting Power Output of Photovoltaic System Using A BP Network Method","volume":"142","author":"Liu","year":"2017","journal-title":"Energy Procedia"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1016\/j.apenergy.2011.12.085","article-title":"A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module","volume":"97","author":"Bonanno","year":"2012","journal-title":"Appl. Energy"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.renene.2006.01.002","article-title":"Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure","volume":"32","author":"Mellita","year":"2007","journal-title":"Renew. Energy"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.renene.2013.04.011","article-title":"Artificial neural network-based model for estimating the produced power of a photovoltaic module","volume":"60","author":"Mellit","year":"2013","journal-title":"Renew. Energy"},{"key":"ref_51","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_52","doi-asserted-by":"crossref","unstructured":"Wang, W., Chung, H.S.-H., Cheng, R., Leung, C.-S., Zhan, X., Lo, A.W.-L., Kwok, J., Xue, C.J., and Zhang, J. (2017, January 1\u20135). Training Neural-Network-Based Controller on Distributed Machine Learning Platform for Power Electronics Systems. Proceedings of the 2017 IEEE Energy Conversion Congress and Exposition (ECCE), Cincinnati, OH, USA.","DOI":"10.1109\/ECCE.2017.8096563"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.solener.2019.12.019","article-title":"Diagnostic Module for Series-Connected Photovoltaic Panels","volume":"196","author":"Garaj","year":"2020","journal-title":"Sol. Energy"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Garaj, M., Hong, K.Y., Chung, H.S.-H., Zhou, J., and Lo, A.W.-L. (2019, January 17\u201321). Photovoltaic Panel Health Diagnostic System for Solar Power Plants. Proceedings of the 34th Annual IEEE Applied Power Electronics Conference and Exposition, Anaheim, CA, USA.","DOI":"10.1109\/APEC.2019.8721839"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Garaj, M., Chung, S.-H.H., Lo, A.W.-L., and Wang, H. (October, January 29). Analysis of solar panel\u2019s lumped equivalent circuit parameters using LASSO. Proceedings of the 2019 IEEE Energy Conversion Congress and Exposition, Baltimore, MD, USA.","DOI":"10.1109\/ECCE.2019.8912913"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Murugadoss, R., and Ramakrishnan, M. (2014, January 18\u201320). Universal approximation of nonlinear system predictions in sigmoid activation functions using artificial neural networks. Proceedings of the 2014 IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, India.","DOI":"10.1109\/ICCIC.2014.7238539"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/7\/3657\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:07:43Z","timestamp":1760123263000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/7\/3657"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,31]]},"references-count":56,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["s23073657"],"URL":"https:\/\/doi.org\/10.3390\/s23073657","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,31]]}}}