EDP Sciences logo
Submit your paper
Search
Menu
Home All issues Volume 10 (2019) Int. J. Simul. Multidisci. Des. Optim., 10 (2019) A6 References
Open Access
Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 10, 2019
Article Number A6
Number of page(s) 9
DOI https://doi.org/10.1051/smdo/2019007
Published online 21 March 2019
  1. X. Liu, An improved perturbation and observation maximum power point tracking algorithm for PV panels, thesis presented for the Degree of Master of Applied Science, Concordia University, Montreal, Quebec, Canada, 2004 [Google Scholar]
  2. A. Pradeep Kumar Yadav, S. Thirumaliah, G. Haritha, Comparison of MPPT algorithms for DC-DC converters based PV systems, Int. J. Adv. Res. Electr. Eectron. Instrum. Eng. 1 , 476 (2012) [Google Scholar]
  3. K.H. Hussein, T. Hashino, Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions, IEE Proc. Gener. Transm. Distrib. 142 , 59–64 (1995) [Google Scholar]
  4. B. Subudhi, R. Pradhan. A comparative study on maximum power point tracking techniques for photovoltaic power systems, IEEE Trans. Sustain. Energy 4 , 89–98 (2013) [CrossRef] [Google Scholar]
  5. S. Lyden, M.E. Haque, A. Gargoom, M. Negnevitsky, Review of maximum power point tracking approaches suitable for PV systems under partial shading conditions, in: Proceedings of the Australasian Universities Power Engineering Conference , Hobart, TAS, Australia, 2013, pp. 1–6 [Google Scholar]
  6. K. Ishaque, Z. Salam, A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition, Renew. Sustain. Energy Rev. 19 , 475–488 (2013) [CrossRef] [Google Scholar]
  7. H.J. El-Khozondar, R.J. El-Khozondar, K. Matter, T. Suntio, A review study of photovoltaic array maximum power tracking algorithms, Renew. Wind Water Sol. 3 , 3 (2016) [CrossRef] [Google Scholar]
  8. A. Soufyane Benyoucef, A. Chouder, K. Kara, S. Silvestre, O.A. Sahed, Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions, Appl. Soft Comput. 32 , 38–48 (2015) [CrossRef] [Google Scholar]
  9. A. Elnosh, V. Khadkikar, W. Xiao, J.L. Kirtely, An improved extremum-seeking based MPPT for grid-connected PV systems with partial shading, in: 2014 IEEE 23rd International Symposium on Industrial Electronics , Istanbul, Turkey, 2014, pp. 2548–2553 [Google Scholar]
  10. G.M. Masters, Renewable and efficient electric power systems (John Wiley & Sons, Inc., Hoboken, NJ, 2004) [CrossRef] [Google Scholar]
  11. R.W. Erickson, DC-DC power converters, in: Wiley Encyclopedia of Electrical and Electronics Engineering (John Wiley & Sons, NY, 2007) [Google Scholar]
  12. M. Lokanadham, K. Vijaya Bhaskar, Incremental conductance based maximum power point tracking (MPPT) for photovoltaic system, Int. J. Eng. Res. Appl. 2 , 1420–1424 (2012) [Google Scholar]
  13. Salazar, F. Tadeo, C. Prada, L. Palacin, Simulation and control of a PV system connected to a low voltage network, Jornadas De Automática, 8–10 September 2010, Jaén, Spain [Google Scholar]
  14. N. Femia, G. Petrone, G. Spagnuolo, M. Vitelli, Optimization of perturb and observe maximum power point tracking method, IEEE Trans. Power Electron. 20 , 963 (2005) [Google Scholar]
  15. A. Chermitti, O. Boukli-Hacene, S. Mouhadjer, Design of a library of components for autonomous photovoltaic system under Matlab/Simulink, Int. J. Comput. Appl. 53 , 13–19 (2012) [Google Scholar]
  16. R.J. Wai, W.H. Wang, C.Y. Lin, High-performance stand-alone photovoltaic generation system, IEEE Trans. Ind. Electron. 55 , 240–250 (2008) [CrossRef] [Google Scholar]
  17. C. Hua, J. Lin, An on-line MPPT algorithm for rapidly changing illuminations of solar arrays, Renew. Energy 28 , 997–1157 (2003) [CrossRef] [Google Scholar]
  18. A. Harrag, S. Messalti, How fuzzy logic can improve PEM fuel cell MPPT performances?, Int. J. Hydrogen Energy 43 , 537 (2017) [CrossRef] [Google Scholar]
  19. A. Harrag, H. Bahri, Novel neural network IC-based variable step size fuel cell MPPT controller: performance, efficiency and lifetime improvement, Int. J. Hydrogen Energy 42 , 3549e63 (2017) [CrossRef] [Google Scholar]
  20. N.H. Saad, A.A. El-Sattar, A.M. Mansour, Adaptive neural controller for maximum power point tracking of ten parameter fuel cell model, J. Electr. Eng. 13 , 1e7 (2013) [Google Scholar]
  21. R.W. Erickson, Fundamentals of power electronics (Chapman & Hall, New York, 1997) [CrossRef] [Google Scholar]
  22. J. Prakash, S.K. Sahoo, S.P. Karthikeyan, I.J. Raglend, Design of PSO-fuzzy MPPT controller for photovoltaic application power electronics and renewable energy systems (Springer, India, 2015), pp. 1339–1348 [Google Scholar]
  23. K. Premkumar, B.V. Manikandan, Adaptive neuro-fuzzy inference system based speed controller for brushless DC motor, Neurocomputing 138 , 260–270 (2014) [CrossRef] [Google Scholar]
  24. A. Chaouachi, R.M. Kamel, K. Nagasaka, A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system, Sol. Energy 84 , 2219–2229 (2010) [CrossRef] [Google Scholar]
  25. C. Larbes, S.A. Cheikh, T. Obeidi, A. Zerguerras, Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system, Renew. Energy 34 , 2093–2100 (2009) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.