{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T10:30:30Z","timestamp":1785753030199,"version":"3.56.0"},"reference-count":30,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"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 this paper, a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault\/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM) and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.<\/jats:p>","DOI":"10.3390\/s21051687","type":"journal-article","created":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T10:25:18Z","timestamp":1614594318000},"page":"1687","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps"],"prefix":"10.3390","volume":"21","author":[{"given":"Alessandro","family":"Betti","sequence":"first","affiliation":[{"name":"i-EM S.r.l. (Intelligence in Energy Management), 57121 Livorno, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5969-5455","authenticated-orcid":false,"given":"Mauro","family":"Tucci","sequence":"additional","affiliation":[{"name":"Department of Energy, Systems, Territory and Construction Engineering (DESTEC), University of Pisa, 56122 Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuele","family":"Crisostomi","sequence":"additional","affiliation":[{"name":"Department of Energy, Systems, Territory and Construction Engineering (DESTEC), University of Pisa, 56122 Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Piazzi","sequence":"additional","affiliation":[{"name":"i-EM S.r.l. 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Photovolt. Res. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lindig, S., Louwen, A., and Moser, D. (2020). Outdoor PV System Monitoring\u2014Input Data Quality, Data Imputation and Filtering Approaches. Energies, 13.","DOI":"10.3390\/en13195099"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ber\u00e1nek, V., Ol\u0161an, T., Libra, M., Poulek, V., Sedl\u00e1\u010dek, J., Dang, M.Q., and Tyukhov, I.I. (2018). New monitoring system for photovoltaic power plants\u2019 management. Energies, 11.","DOI":"10.3390\/en11102495"},{"key":"ref_4","unstructured":"Woyte, A., Richter, M., Moser, D., Mau, S., Reich, N., and Jahn, U. (October, January 30). Monitoring of photovoltaic systems: Good practices and systematic analysis. Proceedings of the 28th European Photovoltaic Solar Energy Conference, Villepinte, France."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Moreno-Garcia, I.M., Palacios-Garcia, E.J., Pallares-Lopez, V., Santiago, I., Gonzalez-Redondo, M.J., Varo-Martinez, M., and Real-Calvo, R.J. (2016). Real-time monitoring system for a utility-scale photovoltaic power plant. Sensors, 16.","DOI":"10.3390\/s16060770"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lazzaretti, A.E., Costa, C.H.D., Rodrigues, M.P., Yamada, G.D., Lexinoski, G., Moritz, G.L., and Santos, R.B.D. (2020). A monitoring system for online fault detection and classification in photovoltaic plants. Sensors, 20.","DOI":"10.3390\/s20174688"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Okere, A., and Iqbal, M.T. (2020). A Review of Conventional Fault Detection Techniques in Solar PV Systems and a Proposal of Long Range (LoRa) Wireless Sensor Network for Module Level Monitoring and Fault Diagnosis in Large Solar PV Farms. Eur. J. Electr. Eng. Comput. Sci., 4.","DOI":"10.24018\/ejece.2020.4.6.267"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gimeno-Sales, F.J., Orts-Grau, S., Escrib\u00e1-Aparisi, A., Gonz\u00e1lez-Altozano, P., Balbastre-Peralta, I., Mart\u00ednez-M\u00e1rquez, C.I., and Segu\u00ed-Chilet, S. (2020). PV Monitoring System for a Water Pumping Scheme with a Lithium-Ion Battery Using Free Open-Source Software and IoT Technologies. Sustainability, 12.","DOI":"10.3390\/su122410651"},{"key":"ref_9","unstructured":"Betti, A., Lo Trovato, M., Leonardi, F.S., Leotta, G., Ruffini, F., and Lanzetta, C. (2017, January 25\u201329). Predictive Maintenance in Photovoltaic Plants with a Big Data Approach. Proceedings of the 33rd European Photovoltaic Solar Energy Conference, Amsterdam, The Netherlands."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.renene.2013.11.073","article-title":"Fault detection method for grid-connected photovoltaic plants","volume":"66","author":"Chine","year":"2014","journal-title":"Renew. Energy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.solener.2016.08.021","article-title":"Fault detection algorithm for grid-connected photovoltaic plants","volume":"137","author":"Dhimish","year":"2016","journal-title":"Sol. Energy"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"Navid, Q., Hassan, A., Fardoun, A.A., and Ramzan, R. (2020). An Online Novel Two-Layered Photovoltaic Fault Monitoring Technique Based Upon the Thermal Signatures. Sustainability, 12.","DOI":"10.3390\/su12229607"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Shao, S., Lu, L., Liu, X., and Zhu, H. (2018). A new PV array fault diagnosis method using fuzzy C-mean clustering and fuzzy membership algorithm. Energies, 11.","DOI":"10.3390\/en11010238"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.enconman.2018.10.040","article-title":"Random forest based intelligent fault diagnosis for PV arrays using array voltage and string currents","volume":"178","author":"Chen","year":"2018","journal-title":"Energy Convers. Manag."},{"key":"ref_16","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":"Zhu","year":"2018","journal-title":"Sol. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1016\/j.enconman.2019.06.062","article-title":"Fault diagnosis for photovoltaic array based on convolutional neural network and electrical time series graph","volume":"196","author":"Lu","year":"2019","journal-title":"Energy Convers. Manag."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"112317","DOI":"10.1016\/j.enconman.2019.112317","article-title":"Multivariate statistical monitoring of photovoltaic plant operation","volume":"205","author":"Taghezouit","year":"2020","journal-title":"Energy Convers. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.renene.2010.05.014","article-title":"The prediction and diagnosis of wind turbine faults","volume":"36","author":"Kusiak","year":"2011","journal-title":"Renew. Energy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1002\/we.319","article-title":"Online wind turbine fault detection through automated SCADA data analysis","volume":"12","author":"Zaher","year":"2009","journal-title":"Wind Energy"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Crespo M\u00e1rquez, A., Gonz\u00e1lez-Prida D\u00edaz, V., and G\u00f3mez Fern\u00e1ndez, J. (2018). Assistance to Dynamic Maintenance Tasks by Ann-Based Models. Advanced Maintenance Modelling for Asset Management, Springer.","DOI":"10.1007\/978-3-319-58045-6"},{"key":"ref_23","first-page":"5","article-title":"K-nearest neighbor in missing data imputation","volume":"5","author":"Malarvizhi","year":"2012","journal-title":"Int. J. Eng. Res. Dev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2541","DOI":"10.1016\/j.jss.2012.05.073","article-title":"Nearest neighbor selection for iteratively kNN imputation","volume":"85","author":"Zhang","year":"2012","journal-title":"J. Syst. Softw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.physa.2007.02.074","article-title":"Detrending moving average algorithm: A closed-form approximation of the scaling law","volume":"382","author":"Arianos","year":"2007","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cowan, G. (1998). Statistical Data Analysis, Oxford University Press.","DOI":"10.1093\/oso\/9780198501565.001.0001"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kohonen, T. (2001). Self-Organizing Maps, Springer. [3rd ed.].","DOI":"10.1007\/978-3-642-56927-2"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1109\/TNN.2010.2046180","article-title":"Adaptive FIR neural model for centroid learning in self-organizing maps","volume":"21","author":"Tucci","year":"2010","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/S0967-0661(02)00141-7","article-title":"A process monitoring system based on the Kohonen self-organizing maps","volume":"11","author":"Vermasvuori","year":"2003","journal-title":"Control. Eng. Pract."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1017\/S0890060417000518","article-title":"Feature evaluation and selection for condition monitoring using a self-organizing map and spatial statistics","volume":"33","author":"Silva","year":"2019","journal-title":"Artif. Intell. Eng. Des. Anal. 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