Metodología de diagnóstico de fallos para sistemas fotovoltaicos de conexión a red

The aim of the present research work is the design of a methodology of fault diagnosis as a contribution to the improvement of indicators about efficiency, maintenance and availability of Photovoltaic Systems of Network Connection (PVSNC). The network connection inverter and the mathematical model o...

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Autores:
Núñez Alvarez, José Ricardo
Benítez P., Israel F
Proenza Y., Roger
Vázquez S., Luis
Díaz M., David
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
spa
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5425
Acceso en línea:
https://hdl.handle.net/11323/5425
https://repositorio.cuc.edu.co/
Palabra clave:
Detección
Aislamiento
Diagnóstico
Identificación
Estimación y acomodación de fallos
Sistemas fotovoltaicos
Monitorización y supervisión
Detection
Isolation
Diagnosis
Identification
Estimation and accommodation of faults
Photovoltaic systems
Monitoring and supervision
Rights
openAccess
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CC0 1.0 Universal
id RCUC2_b7820d82e421378fa06f671cdcc773b9
oai_identifier_str oai:repositorio.cuc.edu.co:11323/5425
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Metodología de diagnóstico de fallos para sistemas fotovoltaicos de conexión a red
title Metodología de diagnóstico de fallos para sistemas fotovoltaicos de conexión a red
spellingShingle Metodología de diagnóstico de fallos para sistemas fotovoltaicos de conexión a red
Detección
Aislamiento
Diagnóstico
Identificación
Estimación y acomodación de fallos
Sistemas fotovoltaicos
Monitorización y supervisión
Detection
Isolation
Diagnosis
Identification
Estimation and accommodation of faults
Photovoltaic systems
Monitoring and supervision
title_short Metodología de diagnóstico de fallos para sistemas fotovoltaicos de conexión a red
title_full Metodología de diagnóstico de fallos para sistemas fotovoltaicos de conexión a red
title_fullStr Metodología de diagnóstico de fallos para sistemas fotovoltaicos de conexión a red
title_full_unstemmed Metodología de diagnóstico de fallos para sistemas fotovoltaicos de conexión a red
title_sort Metodología de diagnóstico de fallos para sistemas fotovoltaicos de conexión a red
dc.creator.fl_str_mv Núñez Alvarez, José Ricardo
Benítez P., Israel F
Proenza Y., Roger
Vázquez S., Luis
Díaz M., David
dc.contributor.author.spa.fl_str_mv Núñez Alvarez, José Ricardo
Benítez P., Israel F
Proenza Y., Roger
Vázquez S., Luis
Díaz M., David
dc.subject.spa.fl_str_mv Detección
Aislamiento
Diagnóstico
Identificación
Estimación y acomodación de fallos
Sistemas fotovoltaicos
Monitorización y supervisión
Detection
Isolation
Diagnosis
Identification
Estimation and accommodation of faults
Photovoltaic systems
Monitoring and supervision
topic Detección
Aislamiento
Diagnóstico
Identificación
Estimación y acomodación de fallos
Sistemas fotovoltaicos
Monitorización y supervisión
Detection
Isolation
Diagnosis
Identification
Estimation and accommodation of faults
Photovoltaic systems
Monitoring and supervision
description The aim of the present research work is the design of a methodology of fault diagnosis as a contribution to the improvement of indicators about efficiency, maintenance and availability of Photovoltaic Systems of Network Connection (PVSNC). The network connection inverter and the mathematical model of the Photovoltaic Generator were firstly analyzed. Afterwards, the existing operational losses of the Photovoltaic Generator were quantified, and the mathematical model was adapted to the real conditions of the System through a polynomial adjustment. A real network connection system of nominal power 7.5 kWp installed at the Research Center of Solar Energy, in the province of Santiago de Cuba, was used to assess the proposed methodology. The results obtained were validated to show that the proposed design successfully supervises the PVSNC.100% of the simulated faults were detected and identified with the designed methodology, whose usefulness was additionally shown when having a maximum rate of 0.22% of false alarm in all the tests done.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-10-08T14:24:39Z
dc.date.available.none.fl_str_mv 2019-10-08T14:24:39Z
dc.date.issued.none.fl_str_mv 2019
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 1697-7920
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dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/5425
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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dc.relation.references.spa.fl_str_mv Alam, M., Khan, F., Johnson, J., Flicker, J., 2015. A comprehensive review of catastrophic faults in PV arrays: types, detection, and mitigation techniques. IEEE Journal of Photovoltaics 5(3):1-16. DOI: 10.1109/JPHOTOV.2015.2397599. Berbesi, T., 2012. Aplicacion de técnicas robustas para detección y diagnóstico de fallos. Tesis Doctoral. Universidad de Valladolid, España. Brooks, B. 2011. The bakersfield fire: a lesson in ground-fault protection. SolarPro, Issue 4.2, Feb/Mar´11. Chao, K., Ho, S., Wang, M., 2008. Modeling and fault diagnosis of a photovoltaic system. Electric Power Systems Research 78 (1), p. 97–105. DOI:10.1016/j.epsr.2006.12.012. Chouder, A., Silvestre, S., 2010. Automatic supervision and fault detection of PV systems based on power losses analysis. Energy Conversion and Management, Volume 51, Issue 10, Pages 1929-1937. DOI: 10.1016/j.enconman.2010.02.025. Chouder, A., Silvestre, S., 2009. S. Analysis model of mismatch power losses in PV systems. Journal of Solar Energy Engineering, 131(2), 024504-5 pages. DOI:10.1115/1.3097275. De Soto, W., Klein, W., Beckman, W. A., 2006. Improvement and Validation of a Model for Photovoltaic Array Performance. Solar Energy, 80(2), Pages 78-88. DOI: 10.1016/j.solener.2005.06.010. Duffie, J. A., Beckman, W. A., 2013. Solar Engineering of Thermal Processes. Fourth Edition. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Farhat, M., Barambones, Ó., Ramos, J., Durán, E., Andújar, J., 2015. Design and Implementation of a Stable Control System based on Fuzzy Logic in order to optimize the performance of a Photovoltaic Generation System. Revista Iberoamericana de Automática e Informática industrial, 12(4), 476-487. DOI:10.1016/j.riai.2015.07.006. Firth, S. K., 2006. Raising Efficiency in Photovoltaic Systems: High Resolution Monitoring and Performance Analysis. Tesis Doctoral. Institute of Energy and Sustainable Development De Montfort University. Garoudja, E., Harrou, F., Sun, Y., Kamel, K., Chouder, A., Silvestre, S., 2017. Statistical fault detection in photovoltaic systems. Solar Energy, 150(1), Pages 485-499. DOI: 10.1016/j.solener.2017.04.043. González, G. N., De Angelo, C. H., Forchetti, D. G., Aligia, D. A., 2018. Detection and Isolation of Faults on the Rotor Side Converter of Doubly Fed Induction Generators. Revista Iberoamericana de Automática e Informática Industrial,15(3), 297-308. ISSN: 1697-7912, DOI: 10.4995/riai.2017.9042. Grimaldo Guerrero, J. W., Mendoza Becerra, M. A., Reyes Calle, W. P., 2017. Modelo para pronosticar la demanda de energía eléctrica utilizando los producto interno brutos sectoriales: Caso de Colombia. Revista Espacios Vol. 38 (22), 38. Guerrero, J. W. G., Toscano, A. D. R., Pacheco, L. V., Tovar, J. O., 2018. Analysis of the Energetic and Productive Effects Derived by the Installation of a Conveyor Belt in the Metal-mechanic Industry. International Journal of Energy Economics and Policy, 8(6), 196-201. DOI: 10.32479/ijeep.7066. Houssein, A., Héraud, N., Souleiman, I., Pellet, G., 2010. Monitoring and fault diagnosis of photovoltaic panels. IEEE International Energy Conference, Manama, pp. 389-394. DOI: 10.1109/ENERGYCON.2010.5771711. Lorenzo, E., Martínez F., Muñoz, J., Narvarte, L., 2007. Predicción y ensayo de la producción de la energía FV conectada a la red. Era solar: Energías renovables, ISSN 0212-4157, Nº. 139, págs. 22-31. 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, vol 67, p. 1–13. DOI: 10.1016/j.simpat.2016.05.005. Meyer, E. L., Van Dyk, E. E., 2004. Assessing the reliability and degradation of photovoltaic module performance parameters, in IEEE Transactions on Reliability, vol. 53, no. 1, pp. 83-92. DOI: 10.1109/TR.2004.824831. Mikati, M., Santos, M., Armenta, C., 2013. Electric grid dependence on the configuration of a small-scale wind and solar power hybrid system. Renewable Energy, 57, 587-593. DOI: 10.1016/j.renene.2013.02.018. Montgomery, D., 2009. Introduction to Statistical Quality Control. Sixth Edition 978-0-470-16992-6 Printed in the United States of America. Munoz, M., Alonso-García, M., Vela, N., Chenlo, F., 2011. Early degradation of silicon pv modules and guaranty conditions. Solar Energy 85(9):2264-2274. DOI: 10.1016/j.solener.2011.06.011. Real Calvo, R., Moreno Muñoz, A., Pallares López, V., González Redondo, M., Moreno García, I., Palacios García, E., 2017. Intelligent Electronic System to Control the Interconnection Between Distributed Generation Resources and Power Grid. Revista Iberoamericana de Automática e Informática Industrial, 14(1):56-69, DOI:10.1016/j.riai.2016.11.002. Romera Cabrerizo, J. A., Santos, M., 2017. ParaTrough: Modelica-based Simulation Library for Solar Thermal Plants. Revista Iberoamericana de Automática e Informática Industrial, 14(4):412-423. DOI: 10.1016/j.riai.2017.06.005. Rubio, F. R., Navas, S. J., Ollero, P., Lemos, J. M., Ortega, M. G., 2018. Optimal Control Applied to Distributed Solar Collector Fields. Revista Iberoamericana de Automática e Informática Industrial, 15(3), 327-338. DOI: 10.4995/riai.2018.8944. Sagastume Gutiérrez, A., Cabello Eras, J.J., Hens, L,. 2017. The Biomass Based Electricity Generation Potential of the Province of Cienfuegos, Cuba. Waste Biomass Valor. 8(6), 2075–2085. https://doi.org/10.1007/s12649-016-9687-x. Sagastume Gutiérrez, A., Cabello Eras, J.J., Huisinghc, D., Vandecasteeled, C., Hense, L., 2018. The current potential of low-carbon economy and biomass-based electricity in Cuba. The case of sugarcane, energy cane and marabu (Dichrostachys cinerea) as biomass sources. Journal of Cleaner Production. 17(2), Pages 716-723. DOI: 10.1016/j.jclepro.2017.11.209. Stettler, S., Toggweiler, P., Wiemken, E., Heidenreich, W., Keizer, A.C., Sark, W.G., Feige, S., Schneider, M., Heilscher, G., É., Lorenz, R., Drews, A., Heinemann, D., 2005. Failure Detection Routine for Grid Connected Pv Systems as Part of the Pvsat2 Project. 20th European Photovoltaic Solar Energy Conference and Exhibition. Tian, H., Mancilla-David, F., Ellis, K., Muljadi, E., Jenkins, P., 2012. Detailed Performance Model for Photovoltaic Systems: Preprint. United States. National Renewable Energy Laboratory. 56 páginas. Vergura, S., Acciani, G., Amoruso, V., Patrono, G., 2008. Inferential statistics for monitoring and fault forecasting of pv plants. In Industrial Electronics IEEE International Symposium on, p. 2414–2419. DOI: 10.1109/ISIE.2008.4677264. Vergura, S., Acciani, G., Amoruso, V., Patrono, G., Vacca, F., 2009. Descriptive and inferential statistics for supervising and monitoring the operation of pv plants. Industrial Electronics, IEEE Transactions on Energy Conversion, p. 4456–4464. DOI: 10.1109/TIE.2008.927404. Zhao, Y., 2010. Fault analysis in solar photovoltaic arrays. Master’s thesis, Northeastern University. Boston, Massachusetts. http://hdl.handle.net/2047/d20003009. Zhao, Y., Ball, R., Mosesian, de Palma, J., Lehman, B., 2014. Graph-based semi-supervised learning for fault detection and classification in solar photovoltaic arrays. In IEEE Transactions on Power Electronics, vol. 30, no. 5, pp. 2848-2858. DOI: 10.1109/TPEL.2014.2364203. Zhao, Y., Lehman, B., Ball, R., Mosesian, J., de Palma, J.F., 2013. Outlier detection rules for fault detection in solar photovoltaic arrays. In Applied Power Electronics Conference and Exposition (APEC), Twenty-Eighth Annual IEEE, p. 2913–2920. DOI: 10.1109/APEC.2013.6520712. Zhao, Y., Yang, L., Lehman, B., de Palma, J., Mosesian, J., 2012. Decision tree-based fault detection and classification in solar photovoltaic arrays. Twenty-Seventh Annual IEEE Applied Power Electronics Conference and Exposition (APEC), Orlando, FL, pp. 93-99. DOI: 10.1109/APEC.2012.6165800.
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spelling Núñez Alvarez, José RicardoBenítez P., Israel FProenza Y., RogerVázquez S., LuisDíaz M., David2019-10-08T14:24:39Z2019-10-08T14:24:39Z20191697-79201697-7912https://hdl.handle.net/11323/5425Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The aim of the present research work is the design of a methodology of fault diagnosis as a contribution to the improvement of indicators about efficiency, maintenance and availability of Photovoltaic Systems of Network Connection (PVSNC). The network connection inverter and the mathematical model of the Photovoltaic Generator were firstly analyzed. Afterwards, the existing operational losses of the Photovoltaic Generator were quantified, and the mathematical model was adapted to the real conditions of the System through a polynomial adjustment. A real network connection system of nominal power 7.5 kWp installed at the Research Center of Solar Energy, in the province of Santiago de Cuba, was used to assess the proposed methodology. The results obtained were validated to show that the proposed design successfully supervises the PVSNC.100% of the simulated faults were detected and identified with the designed methodology, whose usefulness was additionally shown when having a maximum rate of 0.22% of false alarm in all the tests done.Esta investigación tiene como objetivo el diseño de una metodología de diagnóstico de fallos para contribuir al mejoramiento de los indicadores de eficiencia, mantenimiento y disponibilidad de los Sistemas Fotovoltaicos de Conexión a Red (SFVCR). Para lograr dicho objetivo, se realiza el estudio del inversor de conexión a red y del modelo matemático del generador fotovoltaico. Luego se cuantifican las pérdidas operacionales del generador fotovoltaico y se adapta el modelo matemático de éste a las condiciones reales del sistema a través de un ajuste polinomial. Un sistema real de conexión a red de potencia nominal 7.5 kWp, instalado en el Centro de Investigaciones de Energía Solar (CIES) en la provincia Santiago de Cuba, se utiliza para evaluar la metodología propuesta. Con los resultados obtenidos se valida el diseño propuesto para demostrar que éste supervisa con éxito el SFVCR. La metodología fue capaz de detectar e identificar el 100 % de los fallos simulados y los ensayos realizados tuvieron como máximo una tasa de falsa alarma de 0.22 %, evidenciándose su utilidad.Núñez Alvarez, José Ricardo-will be generated-orcid-0000-0002-6607-7305-600Benítez P., Israel FProenza Y., RogerVázquez S., LuisDíaz M., Davidspahttps://doi.org/10.4995/riai.2017.7133Alam, M., Khan, F., Johnson, J., Flicker, J., 2015. A comprehensive review of catastrophic faults in PV arrays: types, detection, and mitigation techniques. IEEE Journal of Photovoltaics 5(3):1-16. DOI: 10.1109/JPHOTOV.2015.2397599. Berbesi, T., 2012. Aplicacion de técnicas robustas para detección y diagnóstico de fallos. Tesis Doctoral. Universidad de Valladolid, España. Brooks, B. 2011. The bakersfield fire: a lesson in ground-fault protection. SolarPro, Issue 4.2, Feb/Mar´11. Chao, K., Ho, S., Wang, M., 2008. Modeling and fault diagnosis of a photovoltaic system. Electric Power Systems Research 78 (1), p. 97–105. DOI:10.1016/j.epsr.2006.12.012. Chouder, A., Silvestre, S., 2010. Automatic supervision and fault detection of PV systems based on power losses analysis. Energy Conversion and Management, Volume 51, Issue 10, Pages 1929-1937. DOI: 10.1016/j.enconman.2010.02.025. Chouder, A., Silvestre, S., 2009. S. Analysis model of mismatch power losses in PV systems. Journal of Solar Energy Engineering, 131(2), 024504-5 pages. DOI:10.1115/1.3097275. De Soto, W., Klein, W., Beckman, W. A., 2006. Improvement and Validation of a Model for Photovoltaic Array Performance. Solar Energy, 80(2), Pages 78-88. DOI: 10.1016/j.solener.2005.06.010. Duffie, J. A., Beckman, W. A., 2013. Solar Engineering of Thermal Processes. Fourth Edition. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Farhat, M., Barambones, Ó., Ramos, J., Durán, E., Andújar, J., 2015. Design and Implementation of a Stable Control System based on Fuzzy Logic in order to optimize the performance of a Photovoltaic Generation System. Revista Iberoamericana de Automática e Informática industrial, 12(4), 476-487. DOI:10.1016/j.riai.2015.07.006. Firth, S. K., 2006. Raising Efficiency in Photovoltaic Systems: High Resolution Monitoring and Performance Analysis. Tesis Doctoral. Institute of Energy and Sustainable Development De Montfort University. Garoudja, E., Harrou, F., Sun, Y., Kamel, K., Chouder, A., Silvestre, S., 2017. Statistical fault detection in photovoltaic systems. Solar Energy, 150(1), Pages 485-499. DOI: 10.1016/j.solener.2017.04.043. González, G. N., De Angelo, C. H., Forchetti, D. G., Aligia, D. A., 2018. Detection and Isolation of Faults on the Rotor Side Converter of Doubly Fed Induction Generators. Revista Iberoamericana de Automática e Informática Industrial,15(3), 297-308. ISSN: 1697-7912, DOI: 10.4995/riai.2017.9042. Grimaldo Guerrero, J. W., Mendoza Becerra, M. A., Reyes Calle, W. P., 2017. Modelo para pronosticar la demanda de energía eléctrica utilizando los producto interno brutos sectoriales: Caso de Colombia. Revista Espacios Vol. 38 (22), 38. Guerrero, J. W. G., Toscano, A. D. R., Pacheco, L. V., Tovar, J. O., 2018. Analysis of the Energetic and Productive Effects Derived by the Installation of a Conveyor Belt in the Metal-mechanic Industry. International Journal of Energy Economics and Policy, 8(6), 196-201. DOI: 10.32479/ijeep.7066. Houssein, A., Héraud, N., Souleiman, I., Pellet, G., 2010. Monitoring and fault diagnosis of photovoltaic panels. IEEE International Energy Conference, Manama, pp. 389-394. DOI: 10.1109/ENERGYCON.2010.5771711. Lorenzo, E., Martínez F., Muñoz, J., Narvarte, L., 2007. Predicción y ensayo de la producción de la energía FV conectada a la red. Era solar: Energías renovables, ISSN 0212-4157, Nº. 139, págs. 22-31. 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, vol 67, p. 1–13. DOI: 10.1016/j.simpat.2016.05.005. Meyer, E. L., Van Dyk, E. E., 2004. Assessing the reliability and degradation of photovoltaic module performance parameters, in IEEE Transactions on Reliability, vol. 53, no. 1, pp. 83-92. DOI: 10.1109/TR.2004.824831. Mikati, M., Santos, M., Armenta, C., 2013. Electric grid dependence on the configuration of a small-scale wind and solar power hybrid system. Renewable Energy, 57, 587-593. DOI: 10.1016/j.renene.2013.02.018. Montgomery, D., 2009. Introduction to Statistical Quality Control. Sixth Edition 978-0-470-16992-6 Printed in the United States of America. Munoz, M., Alonso-García, M., Vela, N., Chenlo, F., 2011. Early degradation of silicon pv modules and guaranty conditions. Solar Energy 85(9):2264-2274. DOI: 10.1016/j.solener.2011.06.011. Real Calvo, R., Moreno Muñoz, A., Pallares López, V., González Redondo, M., Moreno García, I., Palacios García, E., 2017. Intelligent Electronic System to Control the Interconnection Between Distributed Generation Resources and Power Grid. Revista Iberoamericana de Automática e Informática Industrial, 14(1):56-69, DOI:10.1016/j.riai.2016.11.002. Romera Cabrerizo, J. A., Santos, M., 2017. ParaTrough: Modelica-based Simulation Library for Solar Thermal Plants. Revista Iberoamericana de Automática e Informática Industrial, 14(4):412-423. DOI: 10.1016/j.riai.2017.06.005. Rubio, F. R., Navas, S. J., Ollero, P., Lemos, J. M., Ortega, M. G., 2018. Optimal Control Applied to Distributed Solar Collector Fields. Revista Iberoamericana de Automática e Informática Industrial, 15(3), 327-338. DOI: 10.4995/riai.2018.8944. Sagastume Gutiérrez, A., Cabello Eras, J.J., Hens, L,. 2017. The Biomass Based Electricity Generation Potential of the Province of Cienfuegos, Cuba. Waste Biomass Valor. 8(6), 2075–2085. https://doi.org/10.1007/s12649-016-9687-x. Sagastume Gutiérrez, A., Cabello Eras, J.J., Huisinghc, D., Vandecasteeled, C., Hense, L., 2018. The current potential of low-carbon economy and biomass-based electricity in Cuba. The case of sugarcane, energy cane and marabu (Dichrostachys cinerea) as biomass sources. Journal of Cleaner Production. 17(2), Pages 716-723. DOI: 10.1016/j.jclepro.2017.11.209. Stettler, S., Toggweiler, P., Wiemken, E., Heidenreich, W., Keizer, A.C., Sark, W.G., Feige, S., Schneider, M., Heilscher, G., É., Lorenz, R., Drews, A., Heinemann, D., 2005. Failure Detection Routine for Grid Connected Pv Systems as Part of the Pvsat2 Project. 20th European Photovoltaic Solar Energy Conference and Exhibition. Tian, H., Mancilla-David, F., Ellis, K., Muljadi, E., Jenkins, P., 2012. Detailed Performance Model for Photovoltaic Systems: Preprint. United States. National Renewable Energy Laboratory. 56 páginas. Vergura, S., Acciani, G., Amoruso, V., Patrono, G., 2008. Inferential statistics for monitoring and fault forecasting of pv plants. In Industrial Electronics IEEE International Symposium on, p. 2414–2419. DOI: 10.1109/ISIE.2008.4677264. Vergura, S., Acciani, G., Amoruso, V., Patrono, G., Vacca, F., 2009. Descriptive and inferential statistics for supervising and monitoring the operation of pv plants. 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