Have a personal or library account? Click to login

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

Open Access
|Feb 2018

References

  1. 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.
  2. Alik, R. and Jusoh, A. 2017. Modified perturb and observe (P&O) with checking algorithm under various solar irradiation. Solar Energy 148: 128–139.
  3. 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.
  4. 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.
  5. 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): 346–356.
  6. 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.
  7. 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): 2743–2767.
  8. 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: 145–165.
  9. 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.
  10. Filippini, M., Molinas, M. and Oregi, E.O. 2015. A flexible power electronics configuration for coupling renewable energy sources. Electronics 4 (2): 283–302.
  11. 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): 773–787.
  12. 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.
  13. 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: 331–342.
  14. HV Floating MOS-Gate Driver ICs. 2007. International rectifier application note AN-978. Infineon Technologies https://goo.gl/ZdWZ1u.
  15. 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: 227–236.
  16. 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.
  17. 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.
  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: 257–267.
  19. 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): 659–678.
  20. 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.
  21. 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.
  22. 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): 221–233.
  23. 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: 854–867.
  24. 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.
  25. 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: 286–299.
  26. 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.
  27. 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: 1147–1169.
  28. 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.
  29. 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): 701–716.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. Selvan, S., Nair, P. and Umayal 2016. A review on photo voltaic MPPT algorithms. International Journal of Electrical and Computer Engineering 6 (2): 567–582.
  35. 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: 543–550.
  36. Tacca, H.E. 2009. Ferrite toroidal inductor design. IEEE Latin America Transactions 7 (6): 630–635.
  37. 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: 465–479.
  38. 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.
  39. 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.
  40. 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.
Language: English
Page range: 1 - 10
Published on: Feb 28, 2018
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2018 Carlos Robles Algarín, Roberto Liñán Fuentes, Adalberto Ospino Castro, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.