Sistema inteligente para la detección de fallas en sistemas fotovoltaicos
Currently, the number of photovoltaic systems has increased rapidly worldwide, however this broad development has not been supported by improvements in the field of photovoltaic monitoring, which is why this type of systems is still subject to the possibility of failures during its operation. The tr...
- Autores:
-
Trujillo Zucchini, Gabriel De Jesus
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad del Norte
- Repositorio:
- Repositorio Uninorte
- Idioma:
- spa
- OAI Identifier:
- oai:manglar.uninorte.edu.co:10584/8000
- Acceso en línea:
- http://hdl.handle.net/10584/8000
- Palabra clave:
- Lógica Difusa, Sombreado, Cortocircuito, Sobrecarga, Funciones de Membresía
Fuzzy Logic, Shading, Short Circuit, Overload, Membership Functions
- Rights
- License
- Universidad del Norte
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|
dc.title.es_ES.fl_str_mv |
Sistema inteligente para la detección de fallas en sistemas fotovoltaicos |
dc.title.en_US.fl_str_mv |
Intelligent system for the detection of faults in photovoltaic systems |
title |
Sistema inteligente para la detección de fallas en sistemas fotovoltaicos |
spellingShingle |
Sistema inteligente para la detección de fallas en sistemas fotovoltaicos Lógica Difusa, Sombreado, Cortocircuito, Sobrecarga, Funciones de Membresía Fuzzy Logic, Shading, Short Circuit, Overload, Membership Functions |
title_short |
Sistema inteligente para la detección de fallas en sistemas fotovoltaicos |
title_full |
Sistema inteligente para la detección de fallas en sistemas fotovoltaicos |
title_fullStr |
Sistema inteligente para la detección de fallas en sistemas fotovoltaicos |
title_full_unstemmed |
Sistema inteligente para la detección de fallas en sistemas fotovoltaicos |
title_sort |
Sistema inteligente para la detección de fallas en sistemas fotovoltaicos |
dc.creator.fl_str_mv |
Trujillo Zucchini, Gabriel De Jesus |
dc.contributor.advisor.none.fl_str_mv |
Quintero Monroy, Christian Giovanny |
dc.contributor.author.none.fl_str_mv |
Trujillo Zucchini, Gabriel De Jesus |
dc.subject.es_ES.fl_str_mv |
Lógica Difusa, Sombreado, Cortocircuito, Sobrecarga, Funciones de Membresía |
topic |
Lógica Difusa, Sombreado, Cortocircuito, Sobrecarga, Funciones de Membresía Fuzzy Logic, Shading, Short Circuit, Overload, Membership Functions |
dc.subject.en_US.fl_str_mv |
Fuzzy Logic, Shading, Short Circuit, Overload, Membership Functions |
description |
Currently, the number of photovoltaic systems has increased rapidly worldwide, however this broad development has not been supported by improvements in the field of photovoltaic monitoring, which is why this type of systems is still subject to the possibility of failures during its operation. The traditional techniques for the detection of faults in this type of systems are not working properly due to the working conditions when there is low irradiation and the power mppt. Due to the fact that within these photovoltaic systems this type of inconveniences is being presented, the need to investigate new methods that improve the performance, efficiency, reliability and increase the life time of the equipment is relevant. Given this problem, the development of an intelligent system using the fuzzy logic method is considered, there will not be a hardware prototype and the model will only be able to detect shading, short circuit and overload faults within the photovoltaic system. The proposed design is based on a diffuse Mamdani type system, it receives four input variables: irradiation, module temperature, current and voltage. The climate variables were read from the Weather Underground database and the calculation of the module temperature, current and voltage was carried out using mathematical models. Through a series of proposed rules, the three types of faults mentioned above are detected and diagnosed. Through a graphical interface, the behavior of the system is recorded around the clock and the behavior of the I-V and P-V curves is analyzed under normal conditions and during a fault in the operation. The results obtained show that the intelligent system is able to identify 90% accuracy between the types of failure and normal operating situations. |
publishDate |
2018 |
dc.date.accessioned.none.fl_str_mv |
2018-06-01T22:09:52Z |
dc.date.available.none.fl_str_mv |
2018-06-01T22:09:52Z |
dc.date.issued.none.fl_str_mv |
2018-06-01 |
dc.type.es_ES.fl_str_mv |
article |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10584/8000 |
url |
http://hdl.handle.net/10584/8000 |
dc.language.iso.es_ES.fl_str_mv |
spa |
language |
spa |
dc.rights.es_ES.fl_str_mv |
Universidad del Norte |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
Universidad del Norte http://purl.org/coar/access_right/c_abf2 |
dc.publisher.es_ES.fl_str_mv |
Barranquilla, Universidad del norte, 2018 |
institution |
Universidad del Norte |
bitstream.url.fl_str_mv |
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Repositorio Digital de la Universidad del Norte |
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spelling |
Quintero Monroy, Christian GiovannyTrujillo Zucchini, Gabriel De Jesus2018-06-01T22:09:52Z2018-06-01T22:09:52Z2018-06-01http://hdl.handle.net/10584/8000Currently, the number of photovoltaic systems has increased rapidly worldwide, however this broad development has not been supported by improvements in the field of photovoltaic monitoring, which is why this type of systems is still subject to the possibility of failures during its operation. The traditional techniques for the detection of faults in this type of systems are not working properly due to the working conditions when there is low irradiation and the power mppt. Due to the fact that within these photovoltaic systems this type of inconveniences is being presented, the need to investigate new methods that improve the performance, efficiency, reliability and increase the life time of the equipment is relevant. Given this problem, the development of an intelligent system using the fuzzy logic method is considered, there will not be a hardware prototype and the model will only be able to detect shading, short circuit and overload faults within the photovoltaic system. The proposed design is based on a diffuse Mamdani type system, it receives four input variables: irradiation, module temperature, current and voltage. The climate variables were read from the Weather Underground database and the calculation of the module temperature, current and voltage was carried out using mathematical models. Through a series of proposed rules, the three types of faults mentioned above are detected and diagnosed. Through a graphical interface, the behavior of the system is recorded around the clock and the behavior of the I-V and P-V curves is analyzed under normal conditions and during a fault in the operation. The results obtained show that the intelligent system is able to identify 90% accuracy between the types of failure and normal operating situations.En la actualidad, la cantidad de sistemas fotovoltaicos ha aumentado rápidamente a nivel mundial, sin embargo este amplio desarrollo no ha sido respaldado por mejoras en el campo de la monitorización fotovoltaica, por lo cual este tipo de sistemas se encuentra todavía sujeto a la posibilidad de fallas durante su operación. Las técnicas tradicionales para la detección de fallas en este tipo de sistemas no están funcionando adecuadamente debido a las condiciones de trabajo cuando hay baja irradiación y la potencia mppt. Debido que dentro de los sistemas fotovoltaicos se está presentando este tipo de inconvenientes es relevante la necesidad de investigar nuevos métodos que mejoren el rendimiento, la eficiencia, la fiabilidad y aumenten el tiempo de vida de los equipos. Dado este problema, se plantea la elaboración de un sistema inteligente mediante el método de lógica difusa, no se tendrá un prototipo hardware y el modelo solamente será capaz de detectar fallas de tipo sombreado, cortocircuito y sobrecarga dentro del sistema fotovoltaico. El diseño propuesto se basa en un sistema difuso tipo Mamdani, recibe cuatro variables de entrada: irradiación, temperatura del módulo, corriente y voltaje. Se realizó la lectura de las variables climáticas desde la base de datos de Weather Underground y mediante modelos matemáticos se realizó el cálculo de la temperatura del módulo, la corriente y el voltaje. Mediante una serie de reglas propuestas se detectan y diagnostican los tres tipos de fallas anteriormente mencionados. Por medio de una interfaz gráfica se registra el comportamiento del sistema las veinticuatro horas y se analiza el comportamiento de las curvas I-V y P-V en condiciones normales y durante una falla en la operación. Los resultados obtenidos muestran que el sistema inteligente es capaz de identificar con un porcentaje de exactitud del 90% entre los tipos de falla y situaciones normales de operación.spaBarranquilla, Universidad del norte, 2018Universidad del Nortehttp://purl.org/coar/access_right/c_abf2Lógica Difusa, Sombreado, Cortocircuito, Sobrecarga, Funciones de MembresíaFuzzy Logic, Shading, Short Circuit, Overload, Membership FunctionsSistema inteligente para la detección de fallas en sistemas fotovoltaicosIntelligent system for the detection of faults in photovoltaic systemsarticlehttp://purl.org/coar/resource_type/c_6501ORIGINALDetección cortocircuito.pngDetección cortocircuito.pngDetection of short circuit fault within the intelligent system.image/png131585http://172.16.14.36:8080/bitstream/10584/8000/1/Detecci%c3%b3n%20cortocircuito.png0e0a3e8e710a4862ef13765eaa73146eMD51Detección cortocircuito1.pdfDetección cortocircuito1.pdfDetection of short circuit fault within the intelligent system.application/pdf109277http://172.16.14.36:8080/bitstream/10584/8000/2/Detecci%c3%b3n%20cortocircuito1.pdf546e3ff672d14c8cfcb15e99b2c9b228MD52Deteccion cortocircuito español.pngDeteccion cortocircuito español.pngDetección de falla de cortocircuito dentro del sistema inteligente.image/png131635http://172.16.14.36:8080/bitstream/10584/8000/3/Deteccion%20cortocircuito%20espa%c3%b1ol.pngf5ecec7307d14a46c606f7d66ab379a8MD53Detección cortocircuito español.pdfDetección cortocircuito español.pdfDetección de falla de cortocircuito dentro del sistema inteligente.application/pdf108024http://172.16.14.36:8080/bitstream/10584/8000/4/Detecci%c3%b3n%20cortocircuito%20espa%c3%b1ol.pdf3c91d9cf5cb5c356e3d50877061a57f1MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://172.16.14.36:8080/bitstream/10584/8000/5/license.txt8a4605be74aa9ea9d79846c1fba20a33MD5510584/8000oai:172.16.14.36:10584/80002018-06-01 17:09:52.501Repositorio Digital de la Universidad del Nortemauribe@uninorte.edu.co |