Thomas Thangam and K. Muthuvel

View Full Paper


  1. [1] B. Yang, T. Yu, H. Shu, D. Zhu, L. Jiang, Energy reshaping based passive fractional-order PID control design and implementation of a grid-connected PV inverter for MPPT using grouped grey wolf optimizer, Solar Energy, 170, 2018, 31–46 .
  2. [2] M. Al-Dhaifallah, A.M. Nassef, H. Rezk, K.S. Nisar, Optimal parameter design of fractional order control based INC-MPPT for PV system, Solar Energy, 159, 2018, 650–664.
  3. [3] Y.-Y. Hong, A.A. Beltran, and A.C. Paglinawan, A robust design of maximum power point tracking using Taguchi method for stand-alone PV system, Applied Energy, 211, 2018, 50–63.
  4. [4] P. Kofinas, S. Doltsinis, A.I. Dounis, and G.A. Vouros, A reinforcement learning approach for MPPT control method of photovoltaic sources, Renewable Energy, 108, 2017, 461–473.
  5. [5] A. Elrayyah, Y. Sozer, and M. Elbuluk, Microgrid-connected PV-based sources: A novel autonomous control method for maintaining maximum power, IEEE Industry Applications Magazine, 21(2), 2015, 19–29.
  6. [6] Y.-T. Chen, Y.-C. Jhang, and R.-H. Liang, A fuzzy-logic based auto-scaling variable step-size MPPT method for PV systems, Solar Energy, 126, 2016, 53–63.
  7. [7] H. Yatimi and E. Aroudam, Assessment and control of a photovoltaic energy storage system based on the robust sliding mode MPPT controller, Solar Energy, 139, 2016, 557–568.
  8. [8] S. Saravanan and N.R. Babu, RBFN based MPPT algorithm for PV system with high step up converter, Energy Conversion and Management, 122, 2016, 239–251.
  9. [9] Md. F. Ansari, S. Chatterji, and A. Iqbal, A fuzzy logic control scheme for a solar photovoltaic system for a maximum power point tracker, International Journal of Sustainable Energy, 29(4), 2010, 245–255.
  10. [10] C.Y. Yang, C.Y. Hsieh, F.K. Feng, and K.H. Chen, Highly Efficient Analog Maximum Power Point Tracking (AMPPT) in a photovoltaic system, IEEE Transactions on Circuits and Systems I: Regular Papers, 59(7), 2012, 1546–1556.
  11. [11] A.K. Abdelsalam, A.M. Massoud, S. Ahmed, and P.N. Enjeti, High-performance adaptive perturb and observe MPPT technique for photovoltaic-based MGs, IEEE Transactions on Power Electronics, 26(4), 2011, 1010–1021.
  12. [12] T.F. Wu, C.H. Chang, L.C. Lin, and C.L. Kuo, Power loss comparison of singleand two-stage grid-connected photovoltaic systems, IEEE Transactions on Energy Conversion, 26(2), 2011, 707–715.
  13. [13] A.H. El Khateb, N. A. Rahim, and J. Selvaraj, Type-2 fuzzy logic approach of a maximum power point tracking employing SEPIC converter for photovoltaic system, Journal of Clean Energy Technologies, 1(1), 2013.
  14. [14] M. Seyedmahmoudian et al., Simulation and hardware implementation of new maximum power point tracking technique for partially shaded PV system using hybrid DEPSO method, IEEE Transactions on Sustainable Energy, 6(3), 2015, 850– 862.
  15. [15] B. N. Alajmi, K. H. Ahmed, S. J. Finney, and B. W. Williams, Fuzzy-logic-control approach of a modified hill-climbing method for maximum power point in MG standalone photovoltaic system, IEEE Transactions on Power Electronics, 26(4), 2011, 1022–1030.
  16. [16] M. Mohammadi and M. Nafar, Fuzzy sliding-mode based control (FSMC) approach of hybrid micro-grid in power distribution systems, International Journal of Electrical Power & Energy Systems, 51, 2013, 232–242.
  17. [17] A.I. Dounis, P. Kofinas, G. Papadakis, and C. Alafodimos, A direct adaptive neural control for maximum power point tracking of photovoltaic system, Solar Energy, 115, 2015, 145–165.
  18. [18] S. Li, A variable-weather-parameter MPPT control strategy based on MPPT constraint conditions of PV system with inverter, Energy Conversion and Management, 197, 2019, Article 111873.
  19. [19] S.L. Brunton, C.W. Rowley, S.R. Kulkarni, and C. Clarkson, Maximum power point tracking for photovoltaic optimization using ripple-based extremum seeking control, IEEE Transactions on Power Electronics, 25(10), 2010, 2531–2540.
  20. [20] Y. Du, D.D.-C. Lu, G. James, and D.J. Cornforth, Modeling and analysis of current harmonic distortion from grid connected PV inverters under different operating conditions, Solar Energy, 94, 2013, 182–194.
  21. [21] B. Boukezata, J.-P. Gaubert, A. Chaoui, and M. Hachemi, Predictive current control in multifunctional grid connected inverter interfaced by PV system, Solar Energy, 139, 2016, 130–141.
  22. [22] G.J. Kish, J.J. Lee, and P.W. Lehn, Modelling and control of photovoltaic panels utilising the incremental conductance method for maximum power point tracking, IET Renewable Power Generation, 6(4), 2012, 259–266.
  23. [23] S. Jiang, D. Cao, Y. Li, and F.Z. Peng, Grid-connected boosthalf-bridge photovoltaic microinverter system using repetitive current control and maximum power point tracking, IEEE Transactions on Power Electronics, 27(11), 2012, 4711–4722.
  24. [24] I. Fister, I. Fister, X.-S. Yang, and J. Brest, A comprehensive review of firefly algorithms, Swarm and Evolutionary Computation, 13, 2013, 34–46.
  25. [25] S.-C. Chu, P.-w. Tsai, and J.-S. Pan, Cat Swarm Optimization, Conference Paper in Lecture Notes in Computer Science, 12 March 2014.
  26. [26] D. Lalili, A. Mellit, N. Lourci, B. Medjahed, and E.M. Berkouk, Input output feedback linearization control and variable step size MPPT algorithm of a grid-connected photovoltaic inverter, Renewable Energy, 36, 2011, 3282–3291.
  27. [27] R. Kadri, J.P. Gaubert, and G. Champenois, An improved maximum power point tracking for photovoltaic grid-connected inverter based on voltage-oriented control, IEEE Transactions on Industrial Electronics, 58(1), 2011, 66–75. 7
  28. [28] R. Alik and J. Awang, Modified Perturb and Observe (P&O) with checking algorithm under various solar irradiation, Solar Energy, 148, 2017, 128–139.
  29. [29] I. Podlubny, Fractional Differential Equations. Academic Press, New York, 1999.
  30. [30] U.E. Ayten, E. Yuce, and S. Minaei, A voltage-mode PID controller using a single CFOA and only grounded capacitors, Microelectronics Journal, 81, 2018, 84–93.
  31. [31] L. Pappula and D. Ghosh, Cat swarm optimization with normal mutation for fast convergence of multimodal functions, Applied Soft Computing, 66, 2018, 473–491.
  32. [32] M. Vanithasri, R. Balamurugan, and L. Lakshminarasimman, Radial movement optimization (RMO) technique for solving unit commitment problem in power systems, Journal of Electrical Systems and Information Technology, 5(3), 2018, 697–707.

Important Links:

Go Back