Browse > Article
http://dx.doi.org/10.6113/JPE.2019.19.5.1248

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)
Publication Information
Journal of Power Electronics / v.19, no.5, 2019 , pp. 1248-1258 More about this Journal
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
Maximum power point tracking; Moth-flame optimization; Partial shading conditions; Photovoltaic system;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 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.
2 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.   DOI
3 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.   DOI
4 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.   DOI
5 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.   DOI
6 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.   DOI
7 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.   DOI
8 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.   DOI
9 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.   DOI
10 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.   DOI
11 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.   DOI
12 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.   DOI
13 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.   DOI
14 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.
15 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.   DOI
16 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.   DOI
17 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.   DOI
18 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.   DOI
19 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.
20 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.   DOI
21 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.   DOI
22 S Mirjalili, "Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm," Knowledge-Based Syst., Vol. 89, pp. 228-249, Nov. 2015.   DOI
23 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.   DOI
24 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.
25 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.   DOI
26 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.   DOI
27 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.
28 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.
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.   DOI
30 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.   DOI
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.   DOI