Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks

Este 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 vi...

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Autores:: Colmenares Quintero, Ramón Fernando
Rojas-Martinez, Eyberth Rolando
Macho-Hernantes, Fernando
Stansfield, Kim E.
Colmenares-Quintero, Juan Carlos

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2021

Institución:: Universidad Cooperativa de Colombia

Repositorio:: Repositorio UCC

Idioma:

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dc.title.spa.fl_str_mv	Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks
title	Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks
spellingShingle	Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks Sistema fotovoltaico Detección de fallas Red neuronal artificial (ANN) Clasificación Objetivos de Desarrollo Sostenible (ODS). Photovoltaic system fault detection Artificial Neural Network (ANN) classification
title_short	Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks
title_full	Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks
title_fullStr	Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks
title_full_unstemmed	Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks
title_sort	Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks
dc.creator.fl_str_mv	Colmenares Quintero, Ramón Fernando Rojas-Martinez, Eyberth Rolando Macho-Hernantes, Fernando Stansfield, Kim E. Colmenares-Quintero, Juan Carlos
dc.contributor.author.none.fl_str_mv	Colmenares Quintero, Ramón Fernando Rojas-Martinez, Eyberth Rolando Macho-Hernantes, Fernando Stansfield, Kim E. Colmenares-Quintero, Juan Carlos
dc.subject.spa.fl_str_mv	Sistema fotovoltaico Detección de fallas Red neuronal artificial (ANN) Clasificación Objetivos de Desarrollo Sostenible (ODS).
topic	Sistema fotovoltaico Detección de fallas Red neuronal artificial (ANN) Clasificación Objetivos de Desarrollo Sostenible (ODS). Photovoltaic system fault detection Artificial Neural Network (ANN) classification
dc.subject.other.spa.fl_str_mv	Photovoltaic system fault detection Artificial Neural Network (ANN) classification
description	Este 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.
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-12-06T12:34:56Z
dc.date.available.none.fl_str_mv	2021-12-06T12:34:56Z
dc.date.issued.none.fl_str_mv	2021-09-29
dc.type.none.fl_str_mv	Artículos Científicos
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dc.identifier.bibliographicCitation.spa.fl_str_mv	Colmenares-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: 10.1080/23311916.2021.1981520
identifier_str_mv	23311916 Colmenares-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: 10.1080/23311916.2021.1981520
url	https://doi.org/10.1080/23311916.2021.1981520 https://hdl.handle.net/20.500.12494/41026
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dc.relation.ispartofjournal.spa.fl_str_mv	Cogent Engineering
dc.relation.references.spa.fl_str_mv	Ahmed, 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-2019-03091375 Bonsignore, 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.2014.12.405 Chen, 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.2019.111793 Chine, 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.2016.01.036 Chine, 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.2013.11.073 De 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.2018.05.017 Dhimish, 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.2017.10.066 Firth, 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.2009.08.004 Harrou, 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.2017.09.048 Hosenuzzaman, 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.2014.08.046 Jiang, 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. 2015.7280498 Kibaara, 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-2020-01-01-02 Lu, 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.2019.06.062 Madeti, 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.2018.07.038 Mekki, 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.2016.05.005 Pahwa, 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, India Pierdicca, 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-2-893-2018 Rao, 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.2019.8780208 Solórzano, 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.2013.12.020
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spelling	Colmenares Quintero, Ramón FernandoRojas-Martinez, Eyberth RolandoMacho-Hernantes, FernandoStansfield, Kim E.Colmenares-Quintero, Juan Carlos8-12021-12-06T12:34:56Z2021-12-06T12:34:56Z2021-09-2923311916https://doi.org/10.1080/23311916.2021.1981520https://hdl.handle.net/20.500.12494/41026Colmenares-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: 10.1080/23311916.2021.1981520Este 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.Automatic 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.https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000192503https://orcid.org/0000-0003-1166-1982https://sba.minciencias.gov.co/Buscador_Grupos/BuscadorIFindIt/busqueda?q=TERMOMEC&pagenum=1&start=0&type=load&inmeta=COD_ID_GRUPO_s!COL0054239&lang=es&idss=BvCIhKWihGbYwfEramon.colmenaresq@campusucc.edu.cohttps://scholar.google.com/citations?user=9HLAZYUAAAAJ&hl=es1 - 22 p.Taylor & Francis OnlineUniversidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería Civil, Medellín y EnvigadoIngeniería CivilMedellínhttps://www.tandfonline.com/doi/full/10.1080/23311916.2021.1981520Cogent EngineeringAhmed, 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-2019-03091375Bonsignore, 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.2014.12.405Chen, 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.2019.111793Chine, 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.2016.01.036Chine, 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.2013.11.073De 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.2018.05.017Dhimish, 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.2017.10.066Firth, 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.2009.08.004Harrou, 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.2017.09.048Hosenuzzaman, 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.2014.08.046Jiang, 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. 2015.7280498Kibaara, 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-2020-01-01-02Lu, 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.2019.06.062Madeti, 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.2018.07.038Mekki, 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.2016.05.005Pahwa, 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, IndiaPierdicca, 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-2-893-2018Rao, 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.2019.8780208Solórzano, 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.2013.12.020Sistema fotovoltaicoDetección de fallasRed neuronal artificial (ANN)ClasificaciónObjetivos de Desarrollo Sostenible (ODS).Photovoltaic systemfault detectionArtificial Neural Network (ANN)classificationMethodology for automatic fault detection in photovoltaic arrays from artificial neural networksArtículos 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Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks

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