Implementation of a cost-effective fuzzy MPPT controller on the Arduino board

This 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 des...

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Autores:: Robles Algarin, Carlos Arturo
Liñán Fuentes, Roberto
Ospino Castro, Adalberto Jose
Ospino C., Adalberto

Tipo de recurso:: Article of journal

Fecha de publicación:: 2018

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: eng

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network_acronym_str	RCUC2
network_name_str	REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv	Implementation of a cost-effective fuzzy MPPT controller on the Arduino board
dc.title.translated.spa.fl_str_mv	Implementación de un controlador MPPT difuso rentable en la placa Arduino
title	Implementation of a cost-effective fuzzy MPPT controller on the Arduino board
spellingShingle	Implementation of a cost-effective fuzzy MPPT controller on the Arduino board Arduino mega Dc-dc converter Fuzzy logic MPPT controller Photovoltaic module Mega arduino Convertidor dc-dc Lógica difusa Controlador MPPT Módulo fotovoltaico
title_short	Implementation of a cost-effective fuzzy MPPT controller on the Arduino board
title_full	Implementation of a cost-effective fuzzy MPPT controller on the Arduino board
title_fullStr	Implementation of a cost-effective fuzzy MPPT controller on the Arduino board
title_full_unstemmed	Implementation of a cost-effective fuzzy MPPT controller on the Arduino board
title_sort	Implementation of a cost-effective fuzzy MPPT controller on the Arduino board
dc.creator.fl_str_mv	Robles Algarin, Carlos Arturo Liñán Fuentes, Roberto Ospino Castro, Adalberto Jose Ospino C., Adalberto
dc.contributor.author.spa.fl_str_mv	Robles Algarin, Carlos Arturo Liñán Fuentes, Roberto Ospino Castro, Adalberto Jose
dc.contributor.author.none.fl_str_mv	Ospino C., Adalberto
dc.subject.spa.fl_str_mv	Arduino mega Dc-dc converter Fuzzy logic MPPT controller Photovoltaic module Mega arduino Convertidor dc-dc Lógica difusa Controlador MPPT Módulo fotovoltaico
topic	Arduino mega Dc-dc converter Fuzzy logic MPPT controller Photovoltaic module Mega arduino Convertidor dc-dc Lógica difusa Controlador MPPT Módulo fotovoltaico
description	This 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.
publishDate	2018
dc.date.issued.none.fl_str_mv	2018
dc.date.accessioned.none.fl_str_mv	2019-05-22T13:31:18Z
dc.date.available.none.fl_str_mv	2019-05-22T13:31:18Z
dc.type.spa.fl_str_mv	Artículo de revista
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dc.type.content.spa.fl_str_mv	Text
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
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dc.identifier.issn.spa.fl_str_mv	11785608
dc.identifier.uri.spa.fl_str_mv	https://hdl.handle.net/11323/4683
dc.identifier.instname.spa.fl_str_mv	Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv	REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.cuc.edu.co/
identifier_str_mv	11785608 Corporación Universidad de la Costa REDICUC - Repositorio CUC
url	https://hdl.handle.net/11323/4683 https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	Ahmed, 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.
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spelling	Robles Algarin, Carlos ArturoLiñán Fuentes, RobertoOspino Castro, Adalberto JoseOspino C., Adalbertovirtual::895-12019-05-22T13:31:18Z2019-05-22T13:31:18Z201811785608https://hdl.handle.net/11323/4683Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This 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.Este 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.Robles Algarin, Carlos Arturo-9289f664-b72e-4d79-98c9-4f5d2aa70c08-0Liñán Fuentes, Roberto-869cab3e-2405-4d2b-979e-ef314e10dc0a-0Ospino Castro, Adalberto Jose-0000-0003-1466-0424-600engInternational Journal on Smart Sensing and Intelligent SystemsAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Arduino megaDc-dc converterFuzzy logicMPPT controllerPhotovoltaic moduleMega arduinoConvertidor dc-dcLógica difusaControlador MPPTMódulo fotovoltaicoImplementation of a cost-effective fuzzy MPPT controller on the Arduino boardImplementación de un controlador MPPT difuso rentable en la placa ArduinoArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionAhmed, 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. 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Implementation of a cost-effective fuzzy MPPT controller on the Arduino board

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