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...

Full description

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
id REPOUNORT2_a1c6e2b368a9869dda40c08fc2f3e787
oai_identifier_str oai:manglar.uninorte.edu.co:10584/8000
network_acronym_str REPOUNORT2
network_name_str Repositorio Uninorte
repository_id_str
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 http://172.16.14.36:8080/bitstream/10584/8000/1/Detecci%c3%b3n%20cortocircuito.png
http://172.16.14.36:8080/bitstream/10584/8000/2/Detecci%c3%b3n%20cortocircuito1.pdf
http://172.16.14.36:8080/bitstream/10584/8000/3/Deteccion%20cortocircuito%20espa%c3%b1ol.png
http://172.16.14.36:8080/bitstream/10584/8000/4/Detecci%c3%b3n%20cortocircuito%20espa%c3%b1ol.pdf
http://172.16.14.36:8080/bitstream/10584/8000/5/license.txt
bitstream.checksum.fl_str_mv 0e0a3e8e710a4862ef13765eaa73146e
546e3ff672d14c8cfcb15e99b2c9b228
f5ecec7307d14a46c606f7d66ab379a8
3c91d9cf5cb5c356e3d50877061a57f1
8a4605be74aa9ea9d79846c1fba20a33
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Digital de la Universidad del Norte
repository.mail.fl_str_mv mauribe@uninorte.edu.co
_version_ 1808401284734124032
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