Implementation of a cost-effective fuzzy MPPT controller on the Arduino board
Implementación de un controlador MPPT difuso rentable en la placa Arduino
Date
2018
2018
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Abstract
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.
Referencias:
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.
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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