Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
권호 표지
DOI QR코드

DOI QR Code

Moth-Flame Optimization-Based Maximum Power Point Tracking for Photovoltaic Systems Under Partial Shading Conditions

  • Shi, Ji-Ying (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University) ;
  • Zhang, Deng-Yu (China Automotive Technology and Research Center Co., Ltd.) ;
  • Xue, Fei (Electric Power Research Institute, State Grid Ningxia Electric Power Company) ;
  • Li, Ya-Jing (INSPUR Co. Ltd.) ;
  • Qiao, Wen (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University) ;
  • Yang, Wen-Jing (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University) ;
  • Xu, Yi-Ming (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University) ;
  • Yang, Ting (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University)
  • Received : 2018.03.28
  • Accepted : 2018.08.29
  • Published : 2019.09.20
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents a moth-flame optimization (MFO)-based maximum power point tracking (MPPT) method for photovoltaic (PV) systems. The MFO algorithm is a new optimization method that exhibits satisfactory performance in terms of exploration, exploitation, local optima avoidance, and convergence. Therefore, the MFO algorithm is quite suitable for solving multiple peaks of PV systems under partial shading conditions (PSCs). The proposed MFO-MPPT is compared with four MPPT algorithms, namely the perturb and observe (P&O)-MPPT, incremental conductance (INC)-MPPT, particle swarm optimization (PSO)-MPPT and whale optimization algorithm (WOA)-MPPT. Simulation and experiment results demonstrate that the proposed algorithm can extract the global maximum power point (MPP) with greater tracking speed and accuracy under various conditions.

Keywords

References

  1. J. W. Cao, K. Meng, J. Y. Wang, M. B. Yang, Z. Chen, W. Z. LI, and C. Lin, “An energy internet and energy routers,” Scientia Sinica (Informationis), Vol. 44, No. 6, pp. 714-727, Jun. 2014. https://doi.org/10.1360/N112014-00001
  2. D. Verma, S. Nema, A. M. Shandilya, and S. K. Dash, "Maximum power point tracking (MPPT) techniques: Recapitulation in solar photovoltaic systems," Renew. Sustain. Energy Rev., Vol. 54, pp. 1018-1034, Feb. 2016. https://doi.org/10.1016/j.rser.2015.10.068
  3. J. P. Ram, T. S. Babu, and N. Rajasekar, "A comprehensive review on solar PV maximum power point tracking techniques," Renew. Sustain. Energy Rev., Vol. 67, pp. 826-847, Jan. 2017. https://doi.org/10.1016/j.rser.2016.09.076
  4. N. Karami, N. Moubayed, and R. Outbib, "General review and classification of different MPPT Techniques," Renew. Sustain. Energy Rev., Vol. 68, pp. 1-18, Feb. 2017. https://doi.org/10.1016/j.rser.2016.09.132
  5. M. A. M. Ramli, S. Twaha, K. Ishaque, and Y. A. Al-Turki, "A review on maximum power point tracking for photovoltaic systems with and without shading conditions," Renew. Sustain. Energy Rev., Vol. 67, pp. 144-159, Jan. 2017. https://doi.org/10.1016/j.rser.2016.09.013
  6. A. A. Elbaset, H. Ali, M. Abd-El Sattar, and M. Khaled, “Implementation of a modified perturb and observe maximum power point tracking algorithm for photovoltaic system using an embedded microcontroller,” IET Renew. Power Gener., Vol. 10, No. 4, pp. 551-560, Jan. 2016. https://doi.org/10.1049/iet-rpg.2015.0309
  7. J. Ahmed and Z. Salam, “An enhanced adaptive P&O MPPT for fast and efficient tracking under varying environmental conditions,” IEEE Trans. Sustain. Energy, Vol. 9, No. 3, pp. 1487-1496, Jul. 2018. https://doi.org/10.1109/TSTE.2018.2791968
  8. M. A. Elgendy, D. J. Atkinson, and B. Zahawi, “Experimental investigation of the incremental conductance maximum power point tracking algorithm at high perturbation rates,” IET Renew. Power Gener., Vol. 10, No. 2, pp. 133-139, Feb. 2016. https://doi.org/10.1049/iet-rpg.2015.0132
  9. M. A. Ozcelik and A. S. Yilmaz, "Improving the incremental conductance algorithm for two-stage grid-connected photovoltaic systems," Turkish J. Electr. Eng. Comput. Sci., Vol. 26, No. 1, pp. 442-453, Jan. 2018. https://doi.org/10.3906/elk-1412-119
  10. H. Renaudineau, F. Donatantonio, J. Fontchastagner, G. Petrone, G. Spagnuolo, J.-P. Martin, and S. Pierfederici, “A PSO-based global MPPT technique for distributed PV power generation,” IEEE Trans. Ind. Electron., Vol. 62, No. 2, pp. 1047-1058, Feb. 2015. https://doi.org/10.1109/TIE.2014.2336600
  11. J. Y. Shi, W Zhang, Y. G. Zhang, F. Xue, and T. Yang, "MPPT for PV systems based on a dormant PSO algorithm," Electr. Power Syst. Res., Vol. 123, pp. 100-107, Jun. 2015. https://doi.org/10.1016/j.epsr.2015.02.001
  12. R. B. Koad, A. F. Zobaa, and A. El-Shahat, “A novel MPPT algorithm based on particle swarm optimization for photovoltaic systems,” IEEE Trans. Sustain. Energy, Vol. 8, No. 2, pp. 468-476, Apr. 2017. https://doi.org/10.1109/TSTE.2016.2606421
  13. J. Y. Shi, F. Xue, Z.-J. Qin, L.-T. Ling, T. Yang, Y. Wang, and J. Wu, “Tracking the global maximum power point of a photovoltaic system under partial shading conditions using a modified firefly algorithm,” J. Renew. Sustain. Energy, Vol. 8, No. 3, pp. 033501, 2016. https://doi.org/10.1063/1.4948524
  14. D. F. Teshome, C. H. Lee, Y. W. Lin, and K. L. Lian, “A modified firefly algorithm for photovoltaic maximum power point tracking control under partial shading,” IEEE J. Emerg. Sel. Topics Power Electron., Vol. 5, No. 2, pp. 661-671, Jun. 2017. https://doi.org/10.1109/JESTPE.2016.2581858
  15. J. Y. Shi, F. Xue, Z. J. Qin, W. Zhang, L. T. Ling, and T. Yang, “Improved global maximum power point tracking for photovoltaic system via cuckoo search under partial shaded conditions,” J. Power Electron., Vol. 16, No. 1, pp. 287-296, Jan. 2016. https://doi.org/10.6113/JPE.2016.16.1.287
  16. B. R. Peng, K. C. Ho, and Y. H. Liu, “A novel and fast MPPT method suitable for both fast changing and partially shaded conditions,” IEEE Trans. Ind. Electron., Vol. 65, No. 4, pp. 3240-3251, Apr. 2018. https://doi.org/10.1109/TIE.2017.2736484
  17. M Mao, Q Duan, P Duan, and B. Hu, "Comprehensive improvement of artificial fish swarm algorithm for global MPPT in PV system under partial shading conditions," Trans. Inst. Meas. Contr., pp. 0142331217697374, 2017.
  18. M. Mao, Q. Duan, Z. Yang, and P. Duan, "Modelling and global MPPT for PV system under partial shading conditions using modified artificial fish swarm algorithm," Systems Engineering (ISSE), 2016 IEEE International Symposium on. IEEE, 1-7, 2016.
  19. S. K. Cherukuri and S. R. Rayapudi, “Enhanced grey wolf optimizer based MPPT algorithm of PV system under partial shaded condition,” Int. J. Renew. Energy Develop., Vol. 6, No. 3, pp. 203-212, 2017. https://doi.org/10.14710/ijred.6.3.203-212
  20. J. Y. Shi, D. Y. Zhang, L. T. Ling, F. Xue, Y. J. Li, Z. J Qin, and T. Yang, “Dual-algorithm maximum power point tracking control method for photovoltaic systems based on grey wolf optimization and golden-section optimization,” J. Power Electron., Vol. 18, No. 3, pp. 841-852, May 2018. https://doi.org/10.6113/JPE.2018.18.3.841
  21. S. K. Cherukuri and S. R. Rayapudi, “A novel global MPP tracking of photovoltaic system based on whale optimization algorithm,” Int. J. Renew. Energy Develop., Vol. 5, No. 3, pp. 225-232, 2016. https://doi.org/10.14710/ijred.5.3.225-232
  22. S. Gupta and K. Saurabh, "Modified artificial killer whale optimization algorithm for maximum power point tracking under partial shading condition," 2017 International Conference on Recent Trends in Electrical, Electronics and Computing Technologies (ICRTEECT), pp. 87-92, 2017.
  23. N. Kumar, I. Hussain, B. Singh, and B. K. Panigrahi, “MPPT in dynamic condition of partially shaded PV system by using WODE technique,” IEEE Trans. Sustain. Energy, Vol. 8, No. 3, pp. 1204-1214, Jul. 2017. https://doi.org/10.1109/TSTE.2017.2669525
  24. S Mirjalili, "Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm," Knowledge-Based Syst., Vol. 89, pp. 228-249, Nov. 2015. https://doi.org/10.1016/j.knosys.2015.07.006
  25. D. Allam, D. A. Yousri, and M. B. Eteiba, "Parameters extraction of the three diode model for the multi-crystalline solar cell/module using moth-flame optimization algorithm," Energy Convers. Manag., Vol. 123, pp. 535-548, Sep. 2016. https://doi.org/10.1016/j.enconman.2016.06.052
  26. W. Yamany, M. Fawzy, A. Tharwat, and A. E. Hassanien, "Moth-flame optimization for training Multi-Layer Perceptrons," Int. Comput. Eng. Conference. IEEE, pp. 267-272, 2015.
  27. Z. Q. Wang, J. F. Chen, G. F. Zhang, Q. Yang, and Y. H. Dai. "Optimal power flow calculation with moth-flame optimization algorithm," Power Syst. Technol., Vol. 41, No. 11, pp. 3641-3647, 2017.
  28. K. Ishaque, Z. Salam, A. Shamsudin, and M. Amjad, “A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm,” Appl. Energy, Vol. 99, No. 2, pp. 414-422, Nov. 2012. https://doi.org/10.1016/j.apenergy.2012.05.026
  29. F. Keyrouz, “Enhanced bayesian based MPPT controller for PV systems,” IEEE Power Energy Technol. Syst. J., Vol. 5, No. 1, pp. 11-17, Mar. 2018. https://doi.org/10.1109/JPETS.2018.2811708
  30. F. Salem, M. S. A. Moteleb, and H. T. Dorrah, “An enhanced fuzzy-PI controller applied to the MPPT problem,” J. Sci. Eng, Vol. 8, No. 2, pp. 147-153, 2005.
  31. K. Ishaque, Z. Salam, and H. Taheri, “Simple, fast and accurate two-diode model for photovoltaic modules,” Solar Energy Materials and Solar Cells, Vol. 95, No. 2, pp. 586-594, 2011. https://doi.org/10.1016/j.solmat.2010.09.023