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Development of Cuckoo Search MPPT Algorithm for Partially Shaded Solar PV SEPIC Converter

  • CH Hussaian Basha
  • Viraj Bansal
  • C. RaniEmail author
  • R. M. Brisilla
  • S. Odofin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1048)

Abstract

Photovoltaic (PV) power generation is playing a prominent role in rural power generation systems due to its low operating and maintenance cost. The output properties of solar PV mainly depend on solar irradiation, temperature, and load impedance. Hence, the operating point of solar PV oscillates. Due to the oscillatory behavior of operating point, it is difficult to transform maximum power from the source to load. To maintain the operating point constant at the maximum power point (MPP) without oscillations, a maximum power point tracking (MPPT) technique is used. Under partial shading condition, the nonlinear characteristics of PV comprise of multiple maximum power points (MPPs). As a result, discovering true MPP is difficult. The traditional and neural network MPPT methods are not suitable to track the MPP because of oscillations around MPP and impreciseness in tracking under partial shading (PS) condition. Therefore, in this article, a biological intelligence cuckoo search optimization (CSO) technique is utilized to track and extract the maximum power of the solar PV at two PS patterns. MATLAB/Simulink is used to demonstrate the CSO MPPT operation on SEPIC converter.

Keywords

CS MPPT Duty cycle PV cell and Partial shading 

Notes

Acknowledgements

I would like to thank to the University Grants Commission (Govt. of India) for funding my research program and I especially thank VIT University management for providing all the facilities to carry out my research work.

References

  1. 1.
    López, J.M.G., et al.: Smart residential load simulator for energy management in smart grids. IEEE Trans. Ind. Electron. 66(2), 1443–1452 (2019)CrossRefGoogle Scholar
  2. 2.
    Charuchittipan, D., et al.: A semi-empirical model for estimating diffuse solar near infrared radiation in Thailand using ground-and satellite-based data for mapping applications. Renew. Energy 117, 175–183 (2018)CrossRefGoogle Scholar
  3. 3.
    Aliyu, M., et al.: A review of solar-powered water pumping systems. Renew. Sustain. Energy Rev. 87, 61–76 (2018)CrossRefGoogle Scholar
  4. 4.
    Woodruff, D.L., et al.: Constructing probabilistic scenarios for wide-area solar power generation. Solar Energy 160, 153–167 (2018)CrossRefGoogle Scholar
  5. 5.
    Rani, C., Hussaian Basha, C.H., Odofin, S.: Analysis and comparison of SEPIC, Landsman and Zeta converters for PV fed induction motor drive applications. In: 2018 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC). IEEE (2018)Google Scholar
  6. 6.
    Rani, C., Hussaian Basha, C.H.: A review on non-isolated inductor coupled DC-DC converter for photovoltaic grid-connected applications. Int. J. Renew. Energy Res. (IJRER) 7(4), 1570–1585 (2017)Google Scholar
  7. 7.
    Yuan, J., et al.: Coal use for power generation in China. Resour. Conserv. Recycl. 129, 443–453 (2018)CrossRefGoogle Scholar
  8. 8.
    Singh, N.K., Badge, S.S., Salimath, G.F.: Solar tracking for optimizing conversion efficiency using ANN. In: Intelligent Engineering Informatics, pp. 551–559. Springer, Singapore (2018)Google Scholar
  9. 9.
    Tey, K.S., et al.: Improved differential evolution-based MPPT algorithm using SEPIC for PV systems under partial shading conditions and load variation. IEEE Trans. Ind. Inf. (2018)Google Scholar
  10. 10.
    Saravanan, S., Ramesh Babu, N., Sanjeevikumar, P.: Comparative analysis of DC/DC converters with MPPT techniques based PV system. In: Advances in Power Systems and Energy Management, pp. 275–284. Springer, Singapore (2018)Google Scholar
  11. 11.
    Harrag, A., Messalti, S.: How fuzzy logic can improve PEM fuel cell MPPT performances. Int. J. Hydrogen Energy 43(1), 537–550 (2018)CrossRefGoogle Scholar
  12. 12.
    Farayola, A.M., et al.: Distributive MPPT approach using ANFIS and perturb & observe techniques under uniform and partial shading conditions. In: Artificial Intelligence and Evolutionary Computations in Engineering Systems, pp. 27–37. Springer, Singapore (2018)Google Scholar
  13. 13.
    Lee, C.-T., et al.: Application of the hybrid Taguchi genetic algorithm to maximum power point tracking of photovoltaic system. In: 2018 IEEE International Conference on Applied System Invention (ICASI). IEEE (2018)Google Scholar
  14. 14.
    Ebrahim, A.F., et al.: Vector decoupling control design based on genetic algorithm for a residential microgrid system for future city houses at islanding operation. In: SoutheastCon 2018. IEEE (2018)Google Scholar
  15. 15.
    Nguyen, T.T., Vo, D.N., Dinh, B.H.: An effectively adaptive selective cuckoo search algorithm for solving three complicated short-term hydrothermal scheduling problems. Energy 155, 930–956 (2018)CrossRefGoogle Scholar
  16. 16.
    Peng, B.-R., Ho, K.-C., Liu, Y.-H.: A novel and fast MPPT method suitable for both fast changing and partially shaded conditions. IEEE Trans. Ind. Electron. 65(4), 3240–3251 (2018)CrossRefGoogle Scholar
  17. 17.
    Rani, C., Hussaian Basha, C.H., Odofin, S.: Design and switching loss calculation of single leg 3-level 3-phase VSI. In: 2018 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC). IEEE (2018)Google Scholar
  18. 18.
    Ahmed, J., Salam, Z.: A maximum power point tracking (MPPT) for PV system using Cuckoo search with partial shading capability. Appl. Energy 119, 118–130 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • CH Hussaian Basha
    • 1
  • Viraj Bansal
    • 1
  • C. Rani
    • 1
    Email author
  • R. M. Brisilla
    • 1
  • S. Odofin
    • 2
  1. 1.School of Electrical EngineeringVIT UniversityVelloreIndia
  2. 2.School of Energy and EnvironmentUniversity of DerbyDerbyUK

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