Please use this identifier to cite or link to this item: https://repository.ucc.edu.co/handle/20.500.12494/41026
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dc.coverage.temporal8-1es
dc.creatorColmenares-Quintero, Ramón Fernando-
dc.creatorRojas-Martinez, Eyberth Rolando-
dc.creatorMacho-Hernantes, Fernando-
dc.creatorStansfield, Kim E.-
dc.creatorColmenares-Quintero, Juan Carlos-
dc.date.accessioned2021-12-06T12:34:56Z-
dc.date.available2021-12-06T12:34:56Z-
dc.date.issued2021-09-29-
dc.identifier.issn23311916es
dc.identifier.urihttps://doi.org/10.1080/23311916.2021.1981520es
dc.identifier.urihttp://hdl.handle.net/20.500.12494/41026-
dc.descriptionEste artículo presenta una metodología para la detección automática de fallas en matrices fotovoltaicas. Debido a la gran importancia en la construcción de plantas fotovoltaicas cada vez más robustas, la detección automática de averías se ha convertido en una herramienta necesaria para alargar la vida útil de estas plantas, evitar paradas del sistema y reducir graves problemas de seguridad. En el presente estudio se detectan nueve posibles fallas, provocadas por un mal funcionamiento de los diodos de bypass y bloqueo. La solución consiste en entrenar dos modelos basados ​​en redes neuronales artificiales, el primer modelo es un clasificador binario que detecta si ocurre o no una falla, el segundo es un clasificador multiclase que detecta el tipo de falla. Los modelos obtenidos fueron entrenados a partir de datos de simulación, en una arquitectura de 9 paneles fotovoltaicos interconectados en tres filas por matriz de tres columnas (extensible a sistemas más grandes). La evaluación muestra que el sistema de predicción tiene una precisión total del 92,95%. Finalmente, esta metodología se pretende implementar en Colombia, en zonas de difícil acceso y no interconectadas a la red eléctrica, buscando reducir el mantenimiento correctivo.es
dc.description.abstractAutomatic fault detection in photovoltaic (PV) systems has acquired great relevance worldwide, as expressed by (Pierdicca et al., 2018), (Rao et al., 2019), and (Lu et al., 2019). This is due to the necessity of keeping this type of system functioning properly for as long as possible. The early detection of faults in solar plants can be summarized in the reduction of serious safety problems, shutdown of the system and need for corrective maintenance. This will be reflected in the decrease in operating costs.es
dc.description.provenanceSubmitted by ramon colmenaresq (ramon.colmenaresq@campusucc.edu.co) on 2021-12-06T07:11:55Z No. of bitstreams: 2 2021_Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks.pdf: 10374749 bytes, checksum: 10acf0741d6f137ab8924fde9d6ff18e (MD5) Licencia de Uso-Methodology for automatic fault detection in.pdf: 138190 bytes, checksum: 7d428eb5053872e853fb2f329f0bb828 (MD5)en
dc.description.provenanceApproved for entry into archive by Mónica Gómez (monicam.gomez@ucc.edu.co) on 2021-12-06T12:34:56Z (GMT) No. of bitstreams: 2 2021_Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks.pdf: 10374749 bytes, checksum: 10acf0741d6f137ab8924fde9d6ff18e (MD5) Licencia de Uso-Methodology for automatic fault detection in.pdf: 138190 bytes, checksum: 7d428eb5053872e853fb2f329f0bb828 (MD5)en
dc.description.provenanceMade available in DSpace on 2021-12-06T12:34:56Z (GMT). No. of bitstreams: 2 2021_Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks.pdf: 10374749 bytes, checksum: 10acf0741d6f137ab8924fde9d6ff18e (MD5) Licencia de Uso-Methodology for automatic fault detection in.pdf: 138190 bytes, checksum: 7d428eb5053872e853fb2f329f0bb828 (MD5) Previous issue date: 2021-09-29en
dc.format.extent1 - 22 p.es
dc.publisherUniversidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería Civil, Medellín y Envigadoes
dc.relation.ispartofCogent Engineeringes
dc.relation.isversionofhttps://www.tandfonline.com/doi/full/10.1080/23311916.2021.1981520es
dc.subjectSistema fotovoltaicoes
dc.subjectDetección de fallases
dc.subjectRed neuronal artificial (ANN)es
dc.subjectClasificaciónes
dc.subjectObjetivos de Desarrollo Sostenible (ODS).es
dc.subject.otherPhotovoltaic systemes
dc.subject.otherfault detectiones
dc.subject.otherArtificial Neural Network (ANN)es
dc.subject.otherclassificationes
dc.titleMethodology for automatic fault detection in photovoltaic arrays from artificial neural networkses
dc.typeArtículos Científicoses
dc.rights.licenseAtribuciónes
dc.publisher.departmentMedellínes
dc.publisher.programIngeniería Civiles
dc.creator.mailramon.colmenaresq@campusucc.edu.coes
dc.identifier.bibliographicCitationColmenares-Quintero, R.F., Rojas-Martinez, E. R., Macho-Hernantes, F., Stansfield, K.E. y Colmenares-Quintero, J.C. (2021). Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks, Cogent Engineering, 8:1, 1981520, DOI: 10.1080/23311916.2021.1981520es
dc.rights.accessRightsopenAccesses
dc.publisher.editorTaylor & Francis Onlinees
dc.source.bibliographicCitationAhmed, B. M., & Farman Alhialy, N. F. (2019). Optimum efficiency of PV panel using genetic algorithms to Touch Proximate Zero Energy House (NZEH). Civil Engineering Journal, 5(8), 1832–1840. https://doi.org/ 10.28991/cej-2019-03091375es
dc.source.bibliographicCitationBonsignore, L., Davarifar, M., Rabhi, A., Tina, G. M., & Elhajjaji, A. (2014). Neuro-Fuzzy fault detection method for photovoltaic systems. Energy Procedia, 62, 431–441. https://doi.org/10.1016/j.egypro.2014.12.405es
dc.source.bibliographicCitationChen, Z., Chen, Y., Wu, L., Cheng, S., & Lin, P. (2019). Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions. Energy Conversion and Management, 198, 111793. https://doi.org/10.1016/j. enconman.2019.111793es
dc.source.bibliographicCitationChine, W., Mellit, A., Lughi, V., Malek, A., Sulligoi, G., & Massi Pavan, A. (2016). A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks. Renewable Energy, 90, 501–512. https://doi.org/10.1016/j.renene.2016.01.036es
dc.source.bibliographicCitationChine, W., Mellit, A., Pavan, A. M., & Kalogirou, S. A. (2014). Fault detection method for grid-connected photovoltaic plants. Renewable Energy, 66, 99–110. https:// doi.org/10.1016/j.renene.2013.11.073es
dc.source.bibliographicCitationDe Benedetti, M., Leonardi, F., Messina, F., Santoro, C., & Vasilakos, A. (2018). Anomaly detection and predictive maintenance for photovoltaic systems. Neurocomputing, 310, 59–68. https://doi.org/10. 1016/j.neucom.2018.05.017es
dc.source.bibliographicCitationDhimish, M., Holmes, V., Mehrdadi, B., & Dales, M. (2018). Comparing Mamdani Sugeno fuzzy logic and RBF ANN network for PV fault detection. Renewable Energy, 117, 257–274. https://doi.org/10.1016/j. renene.2017.10.066es
dc.source.bibliographicCitationFirth, S. K., Lomas, K. J., & Rees, S. J. (2010). A simple model of PV system performance and its use in fault detection. Solar Energy, 84(4), 624–635. https://doi. org/10.1016/j.solener.2009.08.004es
dc.source.bibliographicCitationHarrou, F., Sun, Y., Taghezouit, B., Saidi, A., & Hamlati, M.- E. (2018). Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches. Renewable Energy, 116, 22–37. https:// doi.org/10.1016/j.renene.2017.09.048es
dc.source.bibliographicCitationHosenuzzaman, M., Rahim, N. A., Selvaraj, J., Hasanuzzaman, M., Malek, A. B. M. A., & Nahar, A. (2015). Global prospects, progress, policies, and environmental impact of solar photovoltaic power generation. Renewable and Sustainable Energy Reviews, 41, 284–297. https://doi.org/10.1016/j.rser.2014.08.046es
dc.source.bibliographicCitationJiang, L. L., & Maskell, D. L. (2015). Automatic fault detection and diagnosis for photovoltaic systems using combined artificial neural network and analytical based methods. 2015 International Joint Conference on Neural Networks (IJCNN), 1–8, Killarney, Ireland. https://doi.org/10.1109/IJCNN. 2015.7280498es
dc.source.bibliographicCitationKibaara, S. K., Murage, D. K., Musau, P., & Saulo, M. J. (2020). Comparative analysis of implementation of solar PV systems using the advanced SPECA modelling tool and HOMER software: Kenyan scenario. HighTech and Innovation Journal, 1(1), 8–20. https://doi.org/10.28991/HIJ-2020-01-01-02es
dc.source.bibliographicCitationLu, X., Lin, P., Cheng, S., Lin, Y., Chen, Z., Wu, L., & Zheng, Q. (2019). Fault diagnosis for photovoltaic array based on convolutional neural network and electrical time series graph. Energy Conversion and Management, 196, 950–965. https://doi.org/10.1016/ j.enconman.2019.06.062es
dc.source.bibliographicCitationMadeti, S. R., & Singh, S. N. (2018). Modeling of PV system based on experimental data for fault detection using kNN method. Solar Energy, 173, 139–151. https://doi. org/10.1016/j.solener.2018.07.038es
dc.source.bibliographicCitationMekki, H., Mellit, A., & Salhi, H. (2016). Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules. Simulation Modelling Practice and Theory, 67, 1–13. https://doi. org/10.1016/j.simpat.2016.05.005es
dc.source.bibliographicCitationPahwa, K., Sharma, M., Saggu, M. S., & Mandpura, A. K. (2020, February). Performance evaluation of machine learning techniques for fault detection and classification in PV array systems. In 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 791–796). IEEE, Noida, Indiaes
dc.source.bibliographicCitationPierdicca, R., Malinverni, E. S., Piccinini, F., Paolanti, M., Felicetti, A., & Zingaretti, P. (2018). DEEP CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC DETECTION OF DAMAGED PHOTOVOLTAIC CELLS. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII–2, XLII-2, 893–900. https://doi.org/10.5194/isprs-archives-XLII-2-893-2018es
dc.source.bibliographicCitationRao, S., Spanias, A., & Tepedelenlioglu, C. (2019). Solar array fault detection using neural networks. 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), 196–200, Taipei, Taiwan. https://doi.org/ 10.1109/ICPHYS.2019.8780208es
dc.source.bibliographicCitationSolórzano, J., & Egido, M. A. (2014). Hot-spot mitigation in PV arrays with distributed MPPT (DMPPT). Solar Energy, 101, 131–137. https://doi.org/10.1016/j.sol ener.2013.12.020es
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dc.description.orcidhttps://orcid.org/0000-0003-1166-1982es
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