Implementación de un controlador MPPT difuso rentable en la placa Arduino

dc.creatorRobles Algarin, Carlos Arturo
dc.creatorLiñán Fuentes, Roberto
dc.creatorOspino Castro, Adalberto Jose
dc.date2019-05-22T13:31:18Z
dc.date2019-05-22T13:31:18Z
dc.date2018
dc.date.accessioned2023-10-03T20:08:49Z
dc.date.available2023-10-03T20:08:49Z
dc.identifier11785608
dc.identifierhttp://hdl.handle.net/11323/4683
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9174522
dc.descriptionThis paper presents the implementation of a fuzzy controller on the Arduino Mega board, for tracking the maximum power point of a photovoltaic (PV) module; using low cost materials. A dc-dc converter that incorporates a driver circuit to control the turning on and offof the Mosfet transistor was designed. The controller was evaluated in a PV system consisting of a 65 W PV module and a 12 V/55Ah battery. The results demonstrate the superiority of the fuzzy controller compared to the traditional P & O algorithm, in terms of efficiency and oscillations around the operating point.
dc.descriptionEste documento presenta la implementación de un controlador difuso en la placa Arduino Mega, para rastrear el punto de máxima potencia de un módulo fotovoltaico (PV); Utilizando materiales de bajo coste. Se diseñó un convertidor dc-dc que incorpora un circuito controlador para controlar el encendido y apagado del transistor Mosfet. El controlador se evaluó en un sistema fotovoltaico que consta de un módulo fotovoltaico de 65 W y una batería de 12 V / 55Ah. Los resultados demuestran la superioridad del controlador difuso en comparación con el algoritmo P & O tradicional, en términos de eficiencia y oscilaciones alrededor del punto de operación.
dc.formatapplication/pdf
dc.languageeng
dc.publisherInternational Journal on Smart Sensing and Intelligent Systems
dc.relationAhmed, J. and Salam, Z. 2015. An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency. Applied Energy 150: 97–108. Alik, R. and Jusoh, A. 2017. Modified perturb and observe (P&O) with checking algorithm under various solar irradiation. Solar Energy 148: 128–39. Atawi, I.E. and Kassem, A.M. 2017. Optimal control based on maximum power point tracking (MPPT) of an autonomous hybrid photovoltaic/storage system in micro grid applications. Energies 10(15): 1–14. Benyoucef, A.S., Chouder, A., Kara, K., Silvestre, S. and Sahed, O.A. 2015. Artificial Bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions. Applied Soft Computing 32: 38–48. Bianconi, E., Calvente, J., Giral, R., Mamarelis, E., Petrone, G., Ramos, C.A., Spagnuolo, G. and Vitelli, M. 2013. Perturb and observe MPPT algorithm with a current controller based on the sliding mode. International Journal of Electrical Power & Energy Systems 44(1): 346–56. Bounechba, H., Bouzid, A., Snani, A. and Lashab, A. 2016. Real time simulation of MPPT algorithms for PV energy system. International Journal of Electrical Power & Energy Systems 83: 67–78. Danandeh, M.A. and Mousavi, S.M. 2018. Comparative and comprehensive review of maximum power point tracking methods for PV cells. Renewable and Sustainable Energy Reviews 82(3): 2743–67. Dounis, A.I., Kofinas, P., Papadakis, G. and Alafodimos, C. 2015. A direct adaptive neural control for maximum power point tracking of photovoltaic system. Solar Energy 115: 145–65. Fathy, A. 2015. Reliable and efficient approach for mitigating the shading effect on photovoltaic module based on Modified Artificial Bee Colony algorithm. Renewable Energy 81: 78–88. Filippini, M., Molinas, M. and Oregi, E.O. 2015. A flexible power electronics configuration for coupling renewable energy sources. Electronics 4(2): 283–302. Haque, A. and Zaheeruddin. 2017. A fast and reliable perturb and observe maximum power point tracker for solar PV system. International Journal of Systems Assurance Engineering and Management 8(2): 773–87. Hassan, S.Z., Li, H., Kamal, T., Arifoğlu, U., Mumtaz, S. and Khan, L. 2017. Neuro-Fuzzy wavelet based adaptive MPPT algorithm for photovoltaic systems. Energies 10(3): 1–16. Huang, Y.P. and Hsu, S.Y. 2016. A performance evaluation model of a high concentration photovoltaic module with a fractional open circuit voltage-based maximum power point tracking algorithm. Computers & Electrical Engineering 51: 331–42. HV Floating MOS-Gate Driver ICs. 2007. International rectifier application note AN-978, Infineon Technologies (https://goo.gl/ZdWZ1u). Jiang, L.L., Maskell, D.L. and Patra, J.C. 2013. A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions. Energy and Buildings 58: 227–36. Jin, Y., Hou, W., Li, G. and Chen, X. 2017. A Glowworm Swarm optimization-based maximum power point tracking for photovoltaic/thermal systems under non-uniform solar irradiation and temperature distribution. Energies 10(4): 1–13. Karami, N., Moubayed, N. and Outbib, R. 2017. General review and classification of different MPPT techniques. Renewable and Sustainable Energy Reviews 68(1): 1–18. Kota, V.R. and Bhukya, M.N. 2017. A novel linear tangents based P&O scheme for MPPT of a PV system. Renewable and Sustainable Energy Reviews 71: 257–67. Lay-Ekuakille, A., Vendramin, G., Fedele, A., Vasanelli, L. and Trotta, A. 2008. PV maximum power point tracking through pyranometric sensor: modelling and characterization. International Journal on Smart Sensing and Intelligent Systems 1(3): 659–78. Loukriz, A., Haddadi, M. and Messalti, S. 2016. Simulation and experimental design of a new advanced variable step size incremental conductance MPPT algorithm for PV systems. ISA Transactions 62: 30–38. Ma, S., Chen, M., Wu, J., Huo, W. and Huang, L. 2016. Augmented nonlinear controller for maximum power-point tracking with artificial neural network in grid-connected photovoltaic systems. Energies 9(12): 1–24. Messaltia, S., Harrag, A. and Loukriz, A. 2017. A new variable step size neural networks MPPT controller: review, simulation and hardware implementation. Renewable and Sustainable Energy Reviews 68(1): 221–33. Mohapatra, A., Nayak, B., Das, P. and Mohanty, K.B. 2017. A review on MPPT techniques of PV system under partial shading condition. Renewable and Sustainable Energy Reviews 80: 854–67. Muñoz, Y., Zafra, D., Acevedo, V. and Ospino, A. 2014. Analysis of energy production with different photovoltaic technologies in the Colombian geography. In IOP Conference Series: Materials Science and Engineering 59(1): 1–9. Muthuramalingam, M. and Manoharan, P.S. 2014. Comparative analysis of distributed MPPT controllers for partially shaded stand alone photovoltaic systems. Energy Conversion and Management 86: 286–99. Na, W., Chen, P. and Kim, J. 2017. An improvement of a Fuzzy Logic-Controlled maximum power point tracking algorithm for photovoltic applications. Applied Science 7(4): 1–17. Nabipour, M., Razaz, M., Seifossadat, S.G. and Mortazavi, S.S. 2017. A new MPPT scheme based on a novel fuzzy approach. Renewable and Sustainable Energy Reviews 74: 1147–69. Ramalu, T., Mohd Radzi, M.A., Mohd Zainuri, M.A.A., Abdul Wahab, N.I. and Abdul Rahman, R.Z. 2016. A photovoltaic-based SEPIC converter with Dual-Fuzzy maximum power point tracking for optimal buck and boost operations. Energies 9(8): 1–17. Ramchandani, V., Pamarthi, K. and Chowdhury, S.R. 2012. Comparative study of maximum power point tracking using Linear Kalman Filter & Unscented Kalman Filter for solar photovoltaic array on field programmable gate array. International Journal on Smart Sensing and Intelligent Systems 5(3): 701–16. Robles Algarín, C., Callejas Cabarcas, J. and Polo Llanos, A. 2017. Low-cost fuzzy logic control for greenhouse environments with web monitoring. Electronics 6(4): 1–12. Robles Algarín, C., Sevilla Hernández, D. and Restrepo Leal, D. 2018. A low-cost maximum power point tracking system based on neural network inverse model controller. Electronics 7(1): 1–17. Robles Algarín, C., Tabard Giraldo, J. and Rodríguez Álvarez, O. 2017. Fuzzy logic based MPPT controller for a PV system. Energies 10(12): 1–18. Robles, C. and Villa, G., 2011. Control del punto de máxima potencia de un panel solar fotovoltaico, utilizando lógica difusa. Telematique 10(2): 54–72. Selvan, S. and Nair, P., Umayal. 2016. A review on photo voltaic MPPT algorithms. International Journal of Electrical and Computer Engineering 6(2): 567–82. Sivakumar, P., Kader, A.A., Kaliavaradhan, Y. and Arutchelvi, M. 2015. Analysis and enhancement of PV efficiency with incremental conductance MPPT technique under non-linear loading conditions. Renewable Energy 81: 543–50. Tacca, H.E. 2009. Ferrite toroidal inductor design. IEEE Latin America Transactions 7(6): 630–35. Titri, S., Larbes, C., Toumi, K.Y. and Benatchba, K. 2017. A new MPPT controller based on the Ant colony optimization algorithm for photovoltaic systems under partial shading conditions. Applied Soft Computing 58: 465–79. Visconti, P., Lay-Ekuakille, A., Primiceri, P. and Cavalera, G. 2016. Wireless energy monitoring system of photovoltaic plants with smart anti-theft solution integrated with control unit of household electrical consumption. International Journal on Smart Sensing and Intelligent Systems 9(2): 681–708. Yaden, M.F., Melhaoui, M., Gaamouche, R., Hirech, K., Baghaz, E. and Kassmi, K. 2013. Photovoltaic system equipped with digital command control and acquisition. Electronics 2(3): 192–211. Yilmaz, U., Kircay, A. and Borekci, S. 2018. PV system fuzzy logic MPPT method and PI control as a charge controller. Renewable and Sustainable Energy Reviews 81(1): 994–1001.
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subjectArduino mega
dc.subjectDc-dc converter
dc.subjectFuzzy logic
dc.subjectMPPT controller
dc.subjectPhotovoltaic module
dc.subjectMega arduino
dc.subjectConvertidor dc-dc
dc.subjectLógica difusa
dc.subjectControlador MPPT
dc.subjectMódulo fotovoltaico
dc.titleImplementation of a cost-effective fuzzy MPPT controller on the Arduino board
dc.titleImplementación de un controlador MPPT difuso rentable en la placa Arduino
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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