Soft Computing for Problem Solving pp 727-736 | Cite as
Development of Cuckoo Search MPPT Algorithm for Partially Shaded Solar PV SEPIC Converter
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 shadingNotes
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.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.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.Aliyu, M., et al.: A review of solar-powered water pumping systems. Renew. Sustain. Energy Rev. 87, 61–76 (2018)CrossRefGoogle Scholar
- 4.Woodruff, D.L., et al.: Constructing probabilistic scenarios for wide-area solar power generation. Solar Energy 160, 153–167 (2018)CrossRefGoogle Scholar
- 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.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.Yuan, J., et al.: Coal use for power generation in China. Resour. Conserv. Recycl. 129, 443–453 (2018)CrossRefGoogle Scholar
- 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.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.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.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.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.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.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.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.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.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.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