Diseño e implementación de una red neuronal para el seguimiento del punto máximo de poder de un panel solar
Este trabajo muestra el diseño e implementación de una red neuronal utilizando método de retropropagación con el fin de realizar el seguimiento de enfoque de luz solar que a su vez permitirá el seguimiento del punto de máxima potencia de un panel solar; esto se logra debido a la capacidad predictiva...
- Autores:
-
Fernández Posada, Santiago
- Tipo de recurso:
- Fecha de publicación:
- 2015
- Institución:
- Universidad Militar Nueva Granada
- Repositorio:
- Repositorio UMNG
- Idioma:
- spa
- OAI Identifier:
- oai:repository.unimilitar.edu.co:10654/6762
- Acceso en línea:
- http://hdl.handle.net/10654/6762
- Palabra clave:
- ENERGIA SOLAR
GENERACION DE ENERGIA FOTOVOLTAICA
Neural network
Gradient descent
Back propagation
learning coefficient
Activation function
Maximum power point
Quality function deployment
radial basis
Redes neuronales
Gradiente descendiente
Retropropagación
Coeficiente de entrenamiento
Función de activación
Punto máximo de poder
Despliegue de la función de calidad
Base radial
- Rights
- License
- http://purl.org/coar/access_right/c_abf2
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| dc.title.spa.fl_str_mv |
Diseño e implementación de una red neuronal para el seguimiento del punto máximo de poder de un panel solar |
| dc.title.translated.spa.fl_str_mv |
Design and implementation of a neural network for the maximum power point tracking of a solar panel |
| title |
Diseño e implementación de una red neuronal para el seguimiento del punto máximo de poder de un panel solar |
| spellingShingle |
Diseño e implementación de una red neuronal para el seguimiento del punto máximo de poder de un panel solar ENERGIA SOLAR GENERACION DE ENERGIA FOTOVOLTAICA Neural network Gradient descent Back propagation learning coefficient Activation function Maximum power point Quality function deployment radial basis Redes neuronales Gradiente descendiente Retropropagación Coeficiente de entrenamiento Función de activación Punto máximo de poder Despliegue de la función de calidad Base radial |
| title_short |
Diseño e implementación de una red neuronal para el seguimiento del punto máximo de poder de un panel solar |
| title_full |
Diseño e implementación de una red neuronal para el seguimiento del punto máximo de poder de un panel solar |
| title_fullStr |
Diseño e implementación de una red neuronal para el seguimiento del punto máximo de poder de un panel solar |
| title_full_unstemmed |
Diseño e implementación de una red neuronal para el seguimiento del punto máximo de poder de un panel solar |
| title_sort |
Diseño e implementación de una red neuronal para el seguimiento del punto máximo de poder de un panel solar |
| dc.creator.fl_str_mv |
Fernández Posada, Santiago |
| dc.contributor.advisor.spa.fl_str_mv |
Mauledoux Monroy, Mauricio Felipe |
| dc.contributor.author.spa.fl_str_mv |
Fernández Posada, Santiago |
| dc.subject.lemb.spa.fl_str_mv |
ENERGIA SOLAR GENERACION DE ENERGIA FOTOVOLTAICA |
| topic |
ENERGIA SOLAR GENERACION DE ENERGIA FOTOVOLTAICA Neural network Gradient descent Back propagation learning coefficient Activation function Maximum power point Quality function deployment radial basis Redes neuronales Gradiente descendiente Retropropagación Coeficiente de entrenamiento Función de activación Punto máximo de poder Despliegue de la función de calidad Base radial |
| dc.subject.keywords.spa.fl_str_mv |
Neural network Gradient descent Back propagation learning coefficient Activation function Maximum power point Quality function deployment radial basis |
| dc.subject.proposal.spa.fl_str_mv |
Redes neuronales Gradiente descendiente Retropropagación Coeficiente de entrenamiento Función de activación Punto máximo de poder Despliegue de la función de calidad Base radial |
| description |
Este trabajo muestra el diseño e implementación de una red neuronal utilizando método de retropropagación con el fin de realizar el seguimiento de enfoque de luz solar que a su vez permitirá el seguimiento del punto de máxima potencia de un panel solar; esto se logra debido a la capacidad predictiva de la red que mediante el uso de sensores de luz lleva a cabo el movimiento angular del panel para encontrar su posición óptima. Todo esto se implementa utilizando un prototipo que contiene: un panel solar, un motor y seis sensores fotovoltaicos. Los sensores son las entradas de la red, que en función de su entrenamiento debe ser capaz de analizar los datos y luego transformarlos en una salida que permita la rotación del motor a la ubicación deseada. |
| publishDate |
2015 |
| dc.date.accessioned.none.fl_str_mv |
2015-11-03T13:52:20Z 2019-12-26T22:11:19Z |
| dc.date.available.none.fl_str_mv |
2015-11-03T13:52:20Z 2019-12-26T22:11:19Z |
| dc.date.issued.none.fl_str_mv |
2015-01-10 |
| dc.type.spa.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
| dc.type.local.spa.fl_str_mv |
Trabajo de grado |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10654/6762 |
| url |
http://hdl.handle.net/10654/6762 |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
H. Ravishankar Kamath, R.S Althal, P.K Singh Ashls Kumar, Sinha y Atlt R Danak, “Modeling of Photovoltaic Array and Maximun Power Point Tracker Using ANN,” de Website: http://journal.esrgroups.org/jes/papers/4_3_4.pdf. A.A. Argiriou, I. Bellas-Velidis y C.A. Balaras, "Development of a neural network heating controller for solar buildings," de Neural Networks 13 (2000) 811-820. H. Wang, T. Luo, Y. Fan, Z. Lu ET. Al.,"A self-powered single-axis maximum power direction tracking system with an on-chip sensor," de Solar Energy, vol. 112, Febrero 2015, p.p. 100-107. Theodore Amissah OCRAN, CAO Junyi CAO Binggang, SUN Xinghua, “Artificial Neural Network Maximum Power Point Tracker for Solar Electric Vehicle”, Apri l 200 5 ECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society,”Artificial neural network based maximum power point tracking technique for PV systems”, 25-28 Oct. 2012 Mohamed Aymen Sahnoun, Hector M. Romero Ugalde, , Jean-Claude Carmona, Julien Gomand, “maximum Power point Tracking Using P&O Control Optimized by a Neural Network Approach: A Good Compromise between Accuracy and Complexity”, 2013. Kuei-Hsiang Chao, Ching-Ju Li, Meng-Huei Wang,A, “Maximum Power Point Tracking Method Based on Extension Neural Network for PV Systems”,2009. Yasushi Kohata, Koichiro Yamauchi, and Masahito Kurihara, “High-Speed Maximum Power Point Tracker for Photovoltaic Systems Using Online Learning Neural Networks”, Mayo 25, 2010. MUMMADI VEERACHARY, TOMONOBU SENJYU, KATSUMI UEZATO “Voltage-Based Maximum Power Point Tracking Control of PV System” M.T. Makhloufi , M.S. Khireddine, Y. Abdessemed, A. Boutarfa, “Tracking Power Photovoltaic System using Artificial Neural Network Control Strategy”, Marzo 12 de 2014. A. Mellita, M. Benghanem,S.A. Kalogirou, "Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure," de Renewable Energy, vol. 32, Febrero 2007, p.p. 285-313. A. Saberian, H. Hizam, M. A. M. Radzi, M. Z. A. Ab Kadir y Maryam Mirzaei, “Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks,” de International Journal of Photoenergy, vol. 2014, Article ID 469701, 10 pages, 2014. Khomdram Jolson, K L Rita, Sapam Jitu, Yengkhom Chandrika, N.Basanta y S.K., “artificial neural network approach for more accurate solar cell electrical circuit model”, International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, Junio 2014. Moufdi Hadjab, Smail Berrah and Hamza Abid, “Neural network for modeling solar panel”, 2012. Fei Wang, Zengqiang Mi, Shi Su y Hongshan Zhao, “Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters”, 15 Febrero 2012 Crescenzio Gallo, Michelangelo De Bonis, “A Neural Network Model for Forecasting Photovoltaic Deployment in Italy” Engin Karatepe,, Mutlu Boztepe, Metin Colak, “Neural network based solar cell model”, Julio de 2005. Shahril Irwan Sulaiman, Nur Zahidah Zainol, Zulkifli Othman, Hedzlin Zainuddin “Modeling of Operating Photovoltaic Module Temperature Using Hybrid Cuckoo and Artificial Neural Network”, 2014. Gwinyai Dzimano, B.S, “Modeling of photovoltaic systems”, 2008. T.M.Vishnukumar y G.Uma, "Intelligent Controller for Maximum Power Point Tracking Control of Solar Power Generation System" de International Journal of Engineering Research and Applications (IJERA), 2013. Anil K. Rai, N.D. Kaushika,Bhupal Singh, Niti Agarwal, "Simulation model of ANN based maximum power point tracking controller for solar PV system," de Solar Energy Materials and Solar Cells, vol. 95, Febrero 2011, p.p. 773-778. CHEHOURI ADAM, GHANDOUR MAZEN , LIVINTI PETRU, “A Real Time Simulation of a Photovoltaic System with Maximum Power Point Tracking”. J. Nagarjuna Reddy, B M Manjunatha, Mallikarjuna Matam, “Improving efficiency of Photovoltaic System with Neural Network Based MPPT Connected To DC Shunt Motor”, Oct. 2013. A. M. Zaki, S. I. Amer y M. Mostafa, "Maximum Power Point Tracking for PV System Using Advanced Neural Networks Technique ," de Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 2, Issue 12, diciembre 2012). Dzung D. Nguyen, Brad Lehman y Sagar Kamarthi, “Performance Evaluation of Solar Photovoltaic Arrays Including Shadow Effects Using Neural Network,” de Energy Conversion Congress and Exposition, 2009. ECCE 2009. IEEE , vol., no., pp.3357,3362, 20-24 Sept. 2009. Dzung D. Nguyen, Brad Lehman, Sagar Kamarthi, “Solar Photovoltaic Array's Shadow Evaluation Using Neural Network with On-Site Measurement”, 2007. Niladri Dey, Dasaradha Ramaiah, Dr. T. Venugopal, “Performance Analysis of Solar Panels on Cloud Services”, Marzo 30, 2015. Jianwu Zeng, Student Member, and Wei Qiao, “Short-Term Solar Power Prediction Using an RBF Neural Network”, 2011. A. Louchene, A. Benmakhlouf y A. Chaghi, “Solar tracking system with fuzzy reasoning applied to crisp sets”, Mayo 2007. [M.Godoy Simoes, N.N.Franceschetti, “Fuzzy optimization based control of a solar array system”. B. Chuco Paucar, J.L. Roel Ortiz, K.S. Collazos L., L.C.Leite y J.O.P Pinto, “Power operation optimization of photovoltaic standalone system with variable loads using fuzzy voltage estimator and neural network controller,” IEEE PowerTech, 2007. C. Salah y M. Ouali, "Comparison of fuzzy logic and neural network in maximum power point tracker for PV systems", de Electric Power Systems Research, vol. 81, Enero 2011, p.p. 43-50. D. Vasarevicius, R. Martavicius y M. Pikutis, “Application of Artificial Neural Networks for Maximum Power Point Tracking of Photovoltaic Panels,” de ELEKTRONIKA IR ELEKTROTECHNIKA, ISSN 1392-1215, VOL. 18, NO. 10, 2012. NAOUFEL KHALDI, HASSAN MAHMOUDI, MALIKA ZAZI, YOUSSEF BARRADI, “Modelling and Analysis of Neural Network and Incremental Conductance MPPT Algorithm for PV Array Using Boost Converter”. Roshan Kini, Geetha.Narayanan, Aditya Dalvi, “COMPARATIVE STUDY AND IMPLEMENTATION OF INCREMENTAL CONDUCTANCE METHOD AND PERTURB AND OBSERVE METHOD WITH BUCK CONVERTER BY USING ARDUINO” Enero-2014, India. Divya Teja Reddy Challa , I. Raghavendar , “Implementation of Incremental Conductance MPPT with Direct Control Method Using Cuk Converter”, Nov-Dic 2012. MERWAN SAAD SAOUD, HADJ AHMED ABBASSI, SALEH KERMICHE, MAHDI OUADA , “Improved incremental conductance method for maximum power point tracking using cuk converter”. Vaddi Ramesh, P Anjappa, P.Dhanamjaya, “Simulation and Implementation of Incremental Conductance MPPT with Direct Control Method Using Boost Converter”, Noviembre 2013. Aymen Chaouachi, Rashad M. Kamel, y Ken Nagasaka, “Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting”, Agosto 4, 2009. S. Premrudeepreechacham y N. Patanapirom, "Solar-Array Modelling and Maximun Power Point Tracking Using Neural Networks", de IEEE Bologna Power Tech Conference, Junio 23-26, 2003, Bologna, Italia. Stuart Russell, Peter Norvig, “Artificial Intelligence A Modern Approach”, Pearson Education Inc, 2010, pp. 728-762. R. Rojas, “Neural Networks a Systematic Introduction”, Springer-Verlag, Berlin, 1996. Cos¸kun Özkan, Filiz Sunar Erbek, “Photogrammetric Engineering & Remote Sensing” Vol. 69, No. 11, November 2003, pp. 1225–1234. Bekir Karlik and A. Vehbi Olgac, “International Journal of Artificial Intelligence And Expert Systems” (IJAE), Volume (1): Issue (4). Chuck Anderson , “CS545: Gradient Descent Department of Computer Science” Colorado State University Fall, 2009. Chenming Hu Richard M. White, “ SOLAR CELLS From Basics to Advanced Systems” McGraw-Hill Series in Electrical Engineering University of California, Berkeley 1983 Bekir Karlik and A. Vehbi Olgac, “Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks” Mario Arrieta Paternina, Luis Carlos Olmos Villalba, Jorge Luis Izquierdo Nuñez, Ramón Antonio Álvarez López, “Design of an solarphotovoltaic system prototype optimizing the slope angle of the solar panels”, 15/05/2012. |
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Ingeniería en Mecatrónica |
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Universidad Militar Nueva Granada |
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Mauledoux Monroy, Mauricio FelipeFernández Posada, SantiagoIngeniero en MecatrónicaCalle 1002015-11-03T13:52:20Z2019-12-26T22:11:19Z2015-11-03T13:52:20Z2019-12-26T22:11:19Z2015-01-10http://hdl.handle.net/10654/6762Este trabajo muestra el diseño e implementación de una red neuronal utilizando método de retropropagación con el fin de realizar el seguimiento de enfoque de luz solar que a su vez permitirá el seguimiento del punto de máxima potencia de un panel solar; esto se logra debido a la capacidad predictiva de la red que mediante el uso de sensores de luz lleva a cabo el movimiento angular del panel para encontrar su posición óptima. Todo esto se implementa utilizando un prototipo que contiene: un panel solar, un motor y seis sensores fotovoltaicos. Los sensores son las entradas de la red, que en función de su entrenamiento debe ser capaz de analizar los datos y luego transformarlos en una salida que permita la rotación del motor a la ubicación deseada.This paper presents the design and implementation of a neural network using backpropagation method in order to track the maximum power point of a solar panel by focusing it to the sunlight; this is achieved due to the prediction ability of the network which will use light sensors in order to rotate the solar panel to its optimal position. All of this is implemented using a prototype that contains: a solar panel, a motor and six photovoltaic sensors. The sensors are the entries of the network that based on its training should be able to analyze the data and then transform it into an output that allows the actuator to rotate the panel to a specific location.Pregradoapplication/pdfspaDiseño e implementación de una red neuronal para el seguimiento del punto máximo de poder de un panel solarDesign and implementation of a neural network for the maximum power point tracking of a solar panelinfo:eu-repo/semantics/bachelorThesisTrabajo de gradohttp://purl.org/coar/resource_type/c_7a1fENERGIA SOLARGENERACION DE ENERGIA FOTOVOLTAICANeural networkGradient descentBack propagationlearning coefficientActivation functionMaximum power pointQuality function deploymentradial basisRedes neuronalesGradiente descendienteRetropropagaciónCoeficiente de entrenamientoFunción de activaciónPunto máximo de poderDespliegue de la función de calidadBase radialFacultad de IngenieríadIngeniería en MecatrónicaUniversidad Militar Nueva GranadaH. Ravishankar Kamath, R.S Althal, P.K Singh Ashls Kumar, Sinha y Atlt R Danak, “Modeling of Photovoltaic Array and Maximun Power Point Tracker Using ANN,” de Website: http://journal.esrgroups.org/jes/papers/4_3_4.pdf.A.A. Argiriou, I. Bellas-Velidis y C.A. Balaras, "Development of a neural network heating controller for solar buildings," de Neural Networks 13 (2000) 811-820.H. Wang, T. Luo, Y. Fan, Z. Lu ET. Al.,"A self-powered single-axis maximum power direction tracking system with an on-chip sensor," de Solar Energy, vol. 112, Febrero 2015, p.p. 100-107.Theodore Amissah OCRAN, CAO Junyi CAO Binggang, SUN Xinghua, “Artificial Neural Network Maximum Power Point Tracker for Solar Electric Vehicle”, Apri l 200 5ECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society,”Artificial neural network based maximum power point tracking technique for PV systems”, 25-28 Oct. 2012Mohamed Aymen Sahnoun, Hector M. Romero Ugalde, , Jean-Claude Carmona, Julien Gomand, “maximum Power point Tracking Using P&O Control Optimized by a Neural Network Approach: A Good Compromise between Accuracy and Complexity”, 2013.Kuei-Hsiang Chao, Ching-Ju Li, Meng-Huei Wang,A, “Maximum Power Point Tracking Method Based on Extension Neural Network for PV Systems”,2009.Yasushi Kohata, Koichiro Yamauchi, and Masahito Kurihara, “High-Speed Maximum Power Point Tracker for Photovoltaic Systems Using Online Learning Neural Networks”, Mayo 25, 2010.MUMMADI VEERACHARY, TOMONOBU SENJYU, KATSUMI UEZATO “Voltage-Based Maximum Power Point Tracking Control of PV System”M.T. Makhloufi , M.S. Khireddine, Y. Abdessemed, A. Boutarfa, “Tracking Power Photovoltaic System using Artificial Neural Network Control Strategy”, Marzo 12 de 2014.A. Mellita, M. Benghanem,S.A. Kalogirou, "Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure," de Renewable Energy, vol. 32, Febrero 2007, p.p. 285-313.A. Saberian, H. Hizam, M. A. M. Radzi, M. Z. A. Ab Kadir y Maryam Mirzaei, “Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks,” de International Journal of Photoenergy, vol. 2014, Article ID 469701, 10 pages, 2014.Khomdram Jolson, K L Rita, Sapam Jitu, Yengkhom Chandrika, N.Basanta y S.K., “artificial neural network approach for more accurate solar cell electrical circuit model”, International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, Junio 2014.Moufdi Hadjab, Smail Berrah and Hamza Abid, “Neural network for modeling solar panel”, 2012.Fei Wang, Zengqiang Mi, Shi Su y Hongshan Zhao, “Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters”, 15 Febrero 2012Crescenzio Gallo, Michelangelo De Bonis, “A Neural Network Model for Forecasting Photovoltaic Deployment in Italy”Engin Karatepe,, Mutlu Boztepe, Metin Colak, “Neural network based solar cell model”, Julio de 2005.Shahril Irwan Sulaiman, Nur Zahidah Zainol, Zulkifli Othman, Hedzlin Zainuddin “Modeling of Operating Photovoltaic Module Temperature Using Hybrid Cuckoo and Artificial Neural Network”, 2014.Gwinyai Dzimano, B.S, “Modeling of photovoltaic systems”, 2008.T.M.Vishnukumar y G.Uma, "Intelligent Controller for Maximum Power Point Tracking Control of Solar Power Generation System" de International Journal of Engineering Research and Applications (IJERA), 2013.Anil K. Rai, N.D. Kaushika,Bhupal Singh, Niti Agarwal, "Simulation model of ANN based maximum power point tracking controller for solar PV system," de Solar Energy Materials and Solar Cells, vol. 95, Febrero 2011, p.p. 773-778.CHEHOURI ADAM, GHANDOUR MAZEN , LIVINTI PETRU, “A Real Time Simulation of a Photovoltaic System with Maximum Power Point Tracking”.J. Nagarjuna Reddy, B M Manjunatha, Mallikarjuna Matam, “Improving efficiency of Photovoltaic System with Neural Network Based MPPT Connected To DC Shunt Motor”, Oct. 2013.A. M. Zaki, S. I. Amer y M. Mostafa, "Maximum Power Point Tracking for PV System Using Advanced Neural Networks Technique ," de Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 2, Issue 12, diciembre 2012).Dzung D. Nguyen, Brad Lehman y Sagar Kamarthi, “Performance Evaluation of Solar Photovoltaic Arrays Including Shadow Effects Using Neural Network,” de Energy Conversion Congress and Exposition, 2009. ECCE 2009. IEEE , vol., no., pp.3357,3362, 20-24 Sept. 2009.Dzung D. Nguyen, Brad Lehman, Sagar Kamarthi, “Solar Photovoltaic Array's Shadow Evaluation Using Neural Network with On-Site Measurement”, 2007.Niladri Dey, Dasaradha Ramaiah, Dr. T. Venugopal, “Performance Analysis of Solar Panels on Cloud Services”, Marzo 30, 2015.Jianwu Zeng, Student Member, and Wei Qiao, “Short-Term Solar Power Prediction Using an RBF Neural Network”, 2011.A. Louchene, A. Benmakhlouf y A. Chaghi, “Solar tracking system with fuzzy reasoning applied to crisp sets”, Mayo 2007.[M.Godoy Simoes, N.N.Franceschetti, “Fuzzy optimization based control of a solar array system”.B. Chuco Paucar, J.L. Roel Ortiz, K.S. Collazos L., L.C.Leite y J.O.P Pinto, “Power operation optimization of photovoltaic standalone system with variable loads using fuzzy voltage estimator and neural network controller,” IEEE PowerTech, 2007.C. Salah y M. Ouali, "Comparison of fuzzy logic and neural network in maximum power point tracker for PV systems", de Electric Power Systems Research, vol. 81, Enero 2011, p.p. 43-50.D. Vasarevicius, R. Martavicius y M. Pikutis, “Application of Artificial Neural Networks for Maximum Power Point Tracking of Photovoltaic Panels,” de ELEKTRONIKA IR ELEKTROTECHNIKA, ISSN 1392-1215, VOL. 18, NO. 10, 2012.NAOUFEL KHALDI, HASSAN MAHMOUDI, MALIKA ZAZI, YOUSSEF BARRADI, “Modelling and Analysis of Neural Network and Incremental Conductance MPPT Algorithm for PV Array Using Boost Converter”.Roshan Kini, Geetha.Narayanan, Aditya Dalvi, “COMPARATIVE STUDY AND IMPLEMENTATION OF INCREMENTAL CONDUCTANCE METHOD AND PERTURB AND OBSERVE METHOD WITH BUCK CONVERTER BY USING ARDUINO” Enero-2014, India.Divya Teja Reddy Challa , I. Raghavendar , “Implementation of Incremental Conductance MPPT with Direct Control Method Using Cuk Converter”, Nov-Dic 2012.MERWAN SAAD SAOUD, HADJ AHMED ABBASSI, SALEH KERMICHE, MAHDI OUADA , “Improved incremental conductance method for maximum power point tracking using cuk converter”.Vaddi Ramesh, P Anjappa, P.Dhanamjaya, “Simulation and Implementation of Incremental Conductance MPPT with Direct Control Method Using Boost Converter”, Noviembre 2013.Aymen Chaouachi, Rashad M. Kamel, y Ken Nagasaka, “Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting”, Agosto 4, 2009.S. Premrudeepreechacham y N. Patanapirom, "Solar-Array Modelling and Maximun Power Point Tracking Using Neural Networks", de IEEE Bologna Power Tech Conference, Junio 23-26, 2003, Bologna, Italia.Stuart Russell, Peter Norvig, “Artificial Intelligence A Modern Approach”, Pearson Education Inc, 2010, pp. 728-762.R. Rojas, “Neural Networks a Systematic Introduction”, Springer-Verlag, Berlin, 1996.Cos¸kun Özkan, Filiz Sunar Erbek, “Photogrammetric Engineering & Remote Sensing” Vol. 69, No. 11, November 2003, pp. 1225–1234.Bekir Karlik and A. Vehbi Olgac, “International Journal of Artificial Intelligence And Expert Systems” (IJAE), Volume (1): Issue (4).Chuck Anderson , “CS545: Gradient Descent Department of Computer Science” Colorado State University Fall, 2009.Chenming Hu Richard M. White, “ SOLAR CELLS From Basics to Advanced Systems” McGraw-Hill Series in Electrical Engineering University of California, Berkeley 1983Bekir Karlik and A. Vehbi Olgac, “Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks”Mario Arrieta Paternina, Luis Carlos Olmos Villalba, Jorge Luis Izquierdo Nuñez, Ramón Antonio Álvarez López, “Design of an solarphotovoltaic system prototype optimizing the slope angle of the solar panels”, 15/05/2012.http://purl.org/coar/access_right/c_abf2ORIGINALInforme_final_Opcion_de_Grado_Fernandez_Posada_1801679.pdfapplication/pdf3379303http://repository.unimilitar.edu.co/bitstream/10654/6762/1/Informe_final_Opcion_de_Grado_Fernandez_Posada_1801679.pdf8dd26e8b176fc9841202b1af3f31a353MD51LICENSElicense.txttext/plain1521http://repository.unimilitar.edu.co/bitstream/10654/6762/2/license.txt57c1b5429c07cf705f9d5e4ce515a2f6MD52TEXTInforme_final_Opcion_de_Grado_Fernandez_Posada_1801679.pdf.txtExtracted texttext/plain165557http://repository.unimilitar.edu.co/bitstream/10654/6762/3/Informe_final_Opcion_de_Grado_Fernandez_Posada_1801679.pdf.txt6dfb7ea9ccceb063c889d703558109bcMD53THUMBNAILInforme_final_Opcion_de_Grado_Fernandez_Posada_1801679.pdf.jpgIM Thumbnailimage/jpeg5445http://repository.unimilitar.edu.co/bitstream/10654/6762/4/Informe_final_Opcion_de_Grado_Fernandez_Posada_1801679.pdf.jpge55e7494eecbda716ff6b75fdae445deMD5410654/6762oai:repository.unimilitar.edu.co:10654/67622020-06-30 13:03:40.401Repositorio Institucional UMNGbibliodigital@unimilitar.edu.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 |