Abstract
The nonlinear characteristics of solar PV consist of different MPPs under the partial shading condition. Hence, it is difficult to find out true MPP. The conventional MPPT methods are not giving an accurate position of MPP. In this work, two global metaheuristic optimization techniques are simulated and the comparative analysis is carried out in terms of tracking speed, steady-state oscillations, algorithm complexity, periodic tuning, and dynamic response. Those are the Cuckoo Search Optimization (CSO) and Particle Swarm Optimization (PSO) MPPT methods used to extract the maximum power of solar PV under partial shading condition. The Matlab/Simulink is used to evaluate performance results of CSA and PSO MPPT techniques.
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Acknowledgements
I would like to thank 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.
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Hussaian Basha, C., Rani, C., Brisilla, R.M., Odofin, S. (2020). Simulation of Metaheuristic Intelligence MPPT Techniques for Solar PV Under Partial Shading Condition. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_63
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DOI: https://doi.org/10.1007/978-981-15-0035-0_63
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