A comparison of global MPPT techniques for partially shaded grid-connected photovoltaic system

Afef Badis, Mohamed Habib Boujmil, Mohamed Nejib Mansouri

Abstract


Maximizing the energy reproduced from a solar power generation system becomes a challenging task when high changes in irradiation or Partial Shading (PS) are experienced. The latter case is considered as one of the unavoidable complicated phenomena since the Photovoltaic (PV) system is extremely affected by displaying numerous local maxima. Thus, it is compulsory to rigorously choose an accurate Maximum Power Point Tracking (MPPT) which identifies effectively the unique Global Point (GP) and avoid any local peaks with the purpose of mitigating the impact of PS. Conventional methods are prone to failure in case of an unpredictable shadow. This paper introduces a comparative assessment of Particle Swarm Optimization (PSO) based MPPT, Genetic Algorithm (GA) based MPPT and P&O for a partially shaded grid-connected photovoltaic system. The main contribution of the paper consists in developing a new variant of PSO algorithm which is a good tradeoff between simplicity, speed, and efficiency. The grid side control is investigated as well through developing different control loops with PID controllers tuned by GA. Various schemes of irradiation and PS are used in order to verify the ability of the threefold algorithms to adequately track the GP.

Keywords


MPPT; Particle Swarm Optimization; Partial shading; Genetic Algorithm; Grid-connected PV systems

Full Text:

PDF

References


M. A. Green, “Photovoltaic principles,†in Physica E: Low-Dimensional Systems and Nanostructures, 2002, vol. 14, no. 1–2, pp. 11–17.

B. Veerasamy, W. Kitagawa, and T. Takeshita, “MPPT method for PV modules using current control-based partial shading detection,†in 3rd International Conference on Renewable Energy Research and Applications (ICRERA), pp. 359–364, 2014.

B. Veerasamy, A. R Thelkar, G. Ramu, and T. Takeshita, "Efficient MPPT control for fast irradiation changes and partial shading conditions on PV systems," In Renewable Energy Research and Applications (ICRERA), 2016 IEEE International Conference, pp. 358-363, IEEE, November 2016.

R. Boukenoui, R. Bradai, A. Mellit, M. Ghanes and H. Salhi, 'Comparative Analysis of P&O, Modified Hill Climbing-FLC, and Adaptive P&O-FLC MPPTs for Microgrid Standalone PV System', 4th International Conference on Renewable Energy Research and Application (ICRERA), Palermo, Italy, pp. 1095-1099, Nov 22-25, 2015.

Abdourraziq, M. A., Ouassaid, M., Maaroufi, M., & Abdourraziq, S. (2013, October). Modified P&O MPPT technique for photovoltaic systems. In Renewable Energy Research and Applications (ICRERA), 2013 International Conference (pp. 728-733). IEEE.

D. Sera, T. Kerekes, R. Teodorescu, and F. Blaabjerg, “Improved MPPT algorithms for rapidly changing environmental conditions,†in EPE-PEMC 2006: 12th International Power Electronics and Motion Control Conference, Proceedings, 2007, pp. 1614–1619.

S. Krim, S. Gdaim, A. Mtibaa and MF. Mimouni, “FPGA Contribution in Photovoltaic Pumping Systems: Models of MPPT and DTC-SVM Algorithms,†International Journal of Renewable Energy Research (IJRER), 6(3), 866-879, 2016.

A. K. Rai, N. D. Kaushika, B. Singh, and N. Agarwal, “Simulation model of ANN based maximum power point tracking controller for solar PV system,†Sol. Energy Mater. Sol. Cells, vol. 95, no. 2, pp. 773–778, 2011.

B. N. Alajmi, K. H. Ahmed, S. J. Finney, B. W. Williams, and B. Wayne Williams, “A maximum power point tracking technique for partially shaded photovoltaic systems in microgrids,†IEEE Trans. Ind. Electron., vol. 60, no. 4, pp. 1596–1606, 2011.

L. L. Jiang, D. L. Maskell, and J. C. Patra, “A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions,†Energy Build., vol. 58, pp. 227–236, 2013.

X. S. Yang, “A new metaheuristic Bat-inspired Algorithm,†in Studies in Computational Intelligence, 2010, vol. 284, pp. 65–74.

L. Zhou, Y. Chen, K. Guo, and F. Jia, “New approach for MPPT control of photovoltaic system with mutative-scale dual-carrier chaotic search,†IEEE Trans. Power Electron., vol. 26, no. 4, pp. 1038–1048, 2011.

K. Ishaque, Z. Salam, M. Amjad, S. Mekhilef, “ An improved particle swarm optimization (PSO)-based MPPT for PV with reduced steady-state oscillationâ€, IEEE Trans Power Electron, vol. 27, no. 8, pp. 3627–38, 2012.

J. Ahmed and Z. Salam, “A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability,†Appl. Energy, vol. 119, pp. 118–130, 2014.

Y. H. Ji, D. Y. Jung, J. G. Kim, J. H. Kim, T. W. Lee, and C. Y. Won, “A real maximum power point tracking method for mismatching compensation in PV array under partially shaded conditions,†IEEE Trans. Power Electron., vol. 26, no. 4, pp. 1001–1009, 2011.

K. Ishaque and Z. Salam, “A Deterministic Particle Swarm Optimization Maximum Power Point Tracker for Photovoltaic System under Partial Shading Condition,†IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 1–1, 2012.

R. Koad, A. F. Zobaa, and A. El Shahat, “A novel MPPT algorithm based on particle swarm optimisation for photovoltaic systems,†IEEE Trans. Sustain. Energy, vol. 8, no. 2, pp. 468–476, 2016.

M. Ben Smida and A. Sakly, “A comparative study of different MPPT methods for grid-connected partially shaded photovoltaic systems,†International Journal of Renewable Energy Research.(IJRER), vol. 6, no. 3, 2016.

A. Badis, M. Boujmil, M. Mansouri, “ A Comparative Study on Maximum Power Point Tracking Techniques of Photovoltaic Systemsâ€, International Journal of Energy Optimization and Engineering, vol. 7, no. 1, pp. 66-85, 2018.

W. Lhomme , P. Delarue , F. Giraud , B. Lemaire-Semail , A. Bouscayrol , “ Simulation of a Photovoltaic Conversion System using Energetic Macroscopic Representationâ€, 15th International Power Electronics and Motion Control Conference (EPE/PEMC), Novi Sad, Serbia, pp. DS3e-7, 4-6 September, 2012.

A. Dolara, F. Grimaccia, M. Mussetta, E. Ogliari, and S. Leva, “An evolutionary-based MPPT algorithm for photovoltaic systems under dynamic partial shading,†Appl. Sci., vol. 8, no. 4, 2018.

P. Lei, Y. Li, and J. E. Seem, “Sequential ESC-based global MPPT control for photovoltaic array with variable shading,†IEEE Trans. Sustainable Energy, vol. 2, no. 3, pp. 348–358, Jul. 2011.

K. Ishaque , Z. Salam , “ A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading conditionâ€, Renewable and Sustainable Energy Reviews, vol. 19, pp. 475–88, 2013.

J. Holland, “ Outline for a Logical Theory of Adaptive Systemsâ€, Journal of the ACM, vol. 9, no.3, pp. 297-314, 1962.

S. Hadji , J.G. Fateh, “ Genetic algorithms for maximum power point tracking in photovoltaic systems Keywordsâ€, Proceedings of World Academy of Science Engineering and Technology, pp. 1–9, October 2011.

A. Badis, M.N. Mansouri, and A. Sakly, “ PSO and GA-based maximum power point tracking for partially shaded photovoltaic systemsâ€, 7th International Renewable Energy Congress (IREC 2016), Hammamet, Tunisia, pp. 1-6, 22-24 March 2016.

A. Badis , M.N. Mansouri , and M.H. Boujmil ,“ A genetic algorithm optimized MPPT controller for a PV system with DC-DC boost converterâ€, International Conference on Engineering & MIS (ICEMIS), Monastir, Tunisia, pp. 1–6, 8-10 May 2017.

S. Choudhury, P.K.Rout, “Adaptive Fuzzy Logic Based MPPT Control for PV System under Partial Shading Condition†International Journal of Renewable Energy Research (IJRER), Vol.5, No.4, 2015.

A. Parisi, L. Curcio, V. Rocca, S. Stivala, A. C. Cino, A. C. Busacca, G. Cipriani, D. La Cascia, V. Di Dio, and R. Miceli, “Photovoltaic module characteristics from CIGS solar cell modelling,†in Proceedings of 2013 International Conference on Renewable Energy Research and Applications (ICRERA), pp. 1139–1144, 2013.




DOI (PDF): https://doi.org/10.20508/ijrer.v8i3.7892.g7443

Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

Publisher: Gazi University

IJRER is cited in SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics);

IJRER has been cited in Emerging Sources Citation Index from 2016 in web of science.

WEB of SCIENCE between 2020-2022; 

h=30,

Average citation per item=5.73

Impact Factor=(1638+1731+1808)/(189+170+221)=9.24

Category Quartile:Q4