Mostrar el registro sencillo del ítem

Diseño e implementación de una red neuronal para el seguimiento del punto máximo de poder de un panel solar

Design and implementation of a neural network for the maximum power point tracking of a solar panel

dc.contributorMauledoux Monroy, Mauricio Felipe
dc.creatorFernández Posada, Santiago
dc.date2015-11-03T13:52:20Z
dc.date2019-12-26T22:11:19Z
dc.date2015-11-03T13:52:20Z
dc.date2019-12-26T22:11:19Z
dc.date2015-01-10
dc.date.accessioned2026-03-25T19:27:54Z
dc.date.available2026-03-25T19:27:54Z
dc.identifierhttps://hdl.handle.net/10654/6762
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/201326
dc.descriptionEste 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.
dc.descriptionThis 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.
dc.descriptionPregrado
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languagespa
dc.publisherIngeniería en Mecatrónica
dc.publisherFacultad de Ingenieríad
dc.publisherUniversidad Militar Nueva Granada
dc.relationH. 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.
dc.relationA.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.
dc.relationH. 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.
dc.relationTheodore Amissah OCRAN, CAO Junyi CAO Binggang, SUN Xinghua, “Artificial Neural Network Maximum Power Point Tracker for Solar Electric Vehicle”, Apri l 200 5
dc.relationECON 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
dc.relationMohamed 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.
dc.relationKuei-Hsiang Chao, Ching-Ju Li, Meng-Huei Wang,A, “Maximum Power Point Tracking Method Based on Extension Neural Network for PV Systems”,2009.
dc.relationYasushi Kohata, Koichiro Yamauchi, and Masahito Kurihara, “High-Speed Maximum Power Point Tracker for Photovoltaic Systems Using Online Learning Neural Networks”, Mayo 25, 2010.
dc.relationMUMMADI VEERACHARY, TOMONOBU SENJYU, KATSUMI UEZATO “Voltage-Based Maximum Power Point Tracking Control of PV System”
dc.relationM.T. Makhloufi , M.S. Khireddine, Y. Abdessemed, A. Boutarfa, “Tracking Power Photovoltaic System using Artificial Neural Network Control Strategy”, Marzo 12 de 2014.
dc.relationA. 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.
dc.relationA. 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.
dc.relationKhomdram 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.
dc.relationMoufdi Hadjab, Smail Berrah and Hamza Abid, “Neural network for modeling solar panel”, 2012.
dc.relationFei 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
dc.relationCrescenzio Gallo, Michelangelo De Bonis, “A Neural Network Model for Forecasting Photovoltaic Deployment in Italy”
dc.relationEngin Karatepe,, Mutlu Boztepe, Metin Colak, “Neural network based solar cell model”, Julio de 2005.
dc.relationShahril Irwan Sulaiman, Nur Zahidah Zainol, Zulkifli Othman, Hedzlin Zainuddin “Modeling of Operating Photovoltaic Module Temperature Using Hybrid Cuckoo and Artificial Neural Network”, 2014.
dc.relationGwinyai Dzimano, B.S, “Modeling of photovoltaic systems”, 2008.
dc.relationT.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.
dc.relationAnil 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.
dc.relationCHEHOURI ADAM, GHANDOUR MAZEN , LIVINTI PETRU, “A Real Time Simulation of a Photovoltaic System with Maximum Power Point Tracking”.
dc.relationJ. Nagarjuna Reddy, B M Manjunatha, Mallikarjuna Matam, “Improving efficiency of Photovoltaic System with Neural Network Based MPPT Connected To DC Shunt Motor”, Oct. 2013.
dc.relationA. 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).
dc.relationDzung 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.
dc.relationDzung D. Nguyen, Brad Lehman, Sagar Kamarthi, “Solar Photovoltaic Array's Shadow Evaluation Using Neural Network with On-Site Measurement”, 2007.
dc.relationNiladri Dey, Dasaradha Ramaiah, Dr. T. Venugopal, “Performance Analysis of Solar Panels on Cloud Services”, Marzo 30, 2015.
dc.relationJianwu Zeng, Student Member, and Wei Qiao, “Short-Term Solar Power Prediction Using an RBF Neural Network”, 2011.
dc.relationA. Louchene, A. Benmakhlouf y A. Chaghi, “Solar tracking system with fuzzy reasoning applied to crisp sets”, Mayo 2007.
dc.relation[M.Godoy Simoes, N.N.Franceschetti, “Fuzzy optimization based control of a solar array system”.
dc.relationB. 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.
dc.relationC. 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.
dc.relationD. 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.
dc.relationNAOUFEL KHALDI, HASSAN MAHMOUDI, MALIKA ZAZI, YOUSSEF BARRADI, “Modelling and Analysis of Neural Network and Incremental Conductance MPPT Algorithm for PV Array Using Boost Converter”.
dc.relationRoshan 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.
dc.relationDivya Teja Reddy Challa , I. Raghavendar , “Implementation of Incremental Conductance MPPT with Direct Control Method Using Cuk Converter”, Nov-Dic 2012.
dc.relationMERWAN SAAD SAOUD, HADJ AHMED ABBASSI, SALEH KERMICHE, MAHDI OUADA , “Improved incremental conductance method for maximum power point tracking using cuk converter”.
dc.relationVaddi Ramesh, P Anjappa, P.Dhanamjaya, “Simulation and Implementation of Incremental Conductance MPPT with Direct Control Method Using Boost Converter”, Noviembre 2013.
dc.relationAymen Chaouachi, Rashad M. Kamel, y Ken Nagasaka, “Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting”, Agosto 4, 2009.
dc.relationS. 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.
dc.relationStuart Russell, Peter Norvig, “Artificial Intelligence A Modern Approach”, Pearson Education Inc, 2010, pp. 728-762.
dc.relationR. Rojas, “Neural Networks a Systematic Introduction”, Springer-Verlag, Berlin, 1996.
dc.relationCos¸kun Özkan, Filiz Sunar Erbek, “Photogrammetric Engineering & Remote Sensing” Vol. 69, No. 11, November 2003, pp. 1225–1234.
dc.relationBekir Karlik and A. Vehbi Olgac, “International Journal of Artificial Intelligence And Expert Systems” (IJAE), Volume (1): Issue (4).
dc.relationChuck Anderson , “CS545: Gradient Descent Department of Computer Science” Colorado State University Fall, 2009.
dc.relationChenming Hu Richard M. White, “ SOLAR CELLS From Basics to Advanced Systems” McGraw-Hill Series in Electrical Engineering University of California, Berkeley 1983
dc.relationBekir Karlik and A. Vehbi Olgac, “Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks”
dc.relationMario 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.
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectENERGIA SOLAR
dc.subjectGENERACION DE ENERGIA FOTOVOLTAICA
dc.subjectNeural network
dc.subjectGradient descent
dc.subjectBack propagation
dc.subjectlearning coefficient
dc.subjectActivation function
dc.subjectMaximum power point
dc.subjectQuality function deployment
dc.subjectradial basis
dc.subjectRedes neuronales
dc.subjectGradiente descendiente
dc.subjectRetropropagación
dc.subjectCoeficiente de entrenamiento
dc.subjectFunción de activación
dc.subjectPunto máximo de poder
dc.subjectDespliegue de la función de calidad
dc.subjectBase radial
dc.titleDiseño e implementación de una red neuronal para el seguimiento del punto máximo de poder de un panel solar
dc.titleDesign and implementation of a neural network for the maximum power point tracking of a solar panel
dc.typeinfo:eu-repo/semantics/bachelorThesis
dc.typeTrabajo de grado
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.coverageCalle 100


Ficheros en el ítem

FicherosTamañoFormatoVer

No hay ficheros asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem