Abstract
This paper gives the realization of the Grey Wolf Optimization (GWO) method for the design of maximum power extraction techniques incorporated in the solar photovoltaic system to extract maximum energy under varying irradiation conditions. The hunting strategy and position updating strategy of the wolves balanced by some crucial mathematical parameters provide a rich diversification and intensification methodology in mapping the search space for the best possible solution. The projected format is considered for a PV array beneath partial shading which displays several peaks, and its tracking concert is related to that of two MPPT algorithms, namely, perturb and observe and Particle swarm optimization technique. The projected GWO tracking algorithm is carried out on a photovoltaic system using MATLAB/Simulink. As of the acquired MATLAB/Simulink end results, it is evaluated that the suggested GWO algorithm beats both Perturb and Observe and Particle swarm optimization (PSO) techniques.
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References
Mohanthy S, Subudhi B, Ray PK (2016) A new MPPT design using Grey Wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Trans Sustain Energy 7(1):181–188
Eltamaly AM, Farh HM (2019) Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC. Sol Energy 177:306–316
Matej C, Liu S-H, Mernik l (2012) A note on teaching learning based optimization algorithm. Inf Sci 212(2012):79–93
Mohammed ANM, Radzi MAM, Azis N, Shafie S, Mohd Zainuri MAA (2020) An enhanced adaptive perturb and observe technique for efficient maximum power point tracking under partial shading condition. Appl Sci 10:3912
Nagadurga T, Narasimham PVRL, Vakula VS (2020) Global maximum power point tracking of solar PV strings using the teaching learning based optimization technique. Int J Ambient Energy. https://doi.org/10.1080/01430750.2020.1721327.
Ahmed F, Ziedan I, Amer D (2020) Improved teaching learning based optimization algorithm–based maximum power point trackers for photovoltaic system. Electr Eng 100:1773–1784
Reddy NN Chandrashekar O, Srujana A (2019) Power quality enhancement by MPC based multilevel control employed with improved particle swarm optimized selective Harmonic elimination. Energy Sources Part A: Recovery Utilization Environ Effects 22(1):23–34
Rao RV, Patel V (2013) An improved teaching –learning – based optimization Algorithm for solving unconstrained optimization problems. Scientia Iranica D 20(3):710–720
Rezk H, Fathy A, Abdelaziz AY (2017) A Comparision of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial Shading conditions. Renew Sustain Energy Rev 74(2017):377–386
Selvamuthukumaran R, Kumar YS, Rajesh G (2016) Global Maximum power point tracking of multiple PV modules under partially shaded condition using stepped comparison search. Electric Power Compon Syst. https://doi.org/10.1080/15325008.2016.1157106
Nafesh A, El-Shafy A (2010) Novel maximum power tracking algorithm for grid- connected photovoltaic system. Int J Green Energy 7(6):600–614
Zhang J, Ding K, Runjie M, Cai Y (2018) Global maximum power point tracking method based on sorting particle swarm optimizer. https://doi.org/10.1080/15435075.2018.1529579
Nagadurga T , Narasimham PVRL , Vakula VS (2019) Harness of maximum solar energy from solar PV strings using particle swarm optimization technique. Int J Ambient Energy https://doi.org/10.1080/01430750.2019.1611643
Mao M, Zhang L, Duan Q, Oghorada OJK, Duan P, Hu B (2017) A two-stage particle awarm optimization algorithm for MPPT of partially shaded PV arrays. Int J Green Energy 14(8):694–702. https://doi.org/10.1080/15435075.2017.1324792
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–64. https://doi.org/10.1016/j.advengsoft.2013.12.007
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Nagadurga, T., Narasimham, P.V.R.L., Vakula, V.S. (2022). Maximum Power Extraction from Solar Photovoltaic Strings Using Grey Wolf Optimization Technique Under Partial Shading Condition. In: Dawn, S., Das, K.N., Mallipeddi, R., Acharjya, D.P. (eds) Smart and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-2109-3_15
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DOI: https://doi.org/10.1007/978-981-16-2109-3_15
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