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An Efficient Honey Badger Optimization Based Solar MPPT Under Partial Shading Conditions

N. Rajeswari1,*, S. Venkatanarayanan2

1 Department of Electrical and Electronics Engineering, Sri Ràaja Raajan College of Engineering and Technology, Sivaganga, Tamilnadu, 630301, India
2 Department of Electrical and Electronics Engineering, K. L. N. College of Engineering, Sivagangai, Tamilnadu, 630612, India

* Corresponding Author: N. Rajeswari. Email: email

Intelligent Automation & Soft Computing 2023, 35(2), 1311-1322. https://doi.org/10.32604/iasc.2023.028552

Abstract

Due to the enormous utilization of solar energy, the photovoltaic (PV) system is used. The PV system is functioned based on a maximum power point (MPP). Due to the climatic change, the Partial shading conditions have occurred under non-uniform irradiance conditions. In the PV system, the global maximum power point (GMPP) is complex to track in the P-V curve due to the Partial shading. Therefore, several tracking processes are performed using various methods like perturb and observe (P & O), hill climbing (HC), incremental conductance (INC), Fuzzy Logic, Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Flying Squirrel Search Optimization (FSSO) etc. Though, the MPPT is not so efficient when the partial shading is increased. To increase the efficiency and convergences in MMPT, the Honey Badger optimization (HBO) algorithm is presented. This HBO model is motivated by the excellent foraging behaviour of honey badgers. This HBO model is used to achieve the best solution in GMPP tracking and speed convergence. The HBO methodology is also compared with prior P&O, WOA and FSSO methods using MATLAB. Therefore, the experiment shows that the HBO method is performed a higher tracking than all prior methods.

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Cite This Article

N. Rajeswari and S. Venkatanarayanan, "An efficient honey badger optimization based solar mppt under partial shading conditions," Intelligent Automation & Soft Computing, vol. 35, no.2, pp. 1311–1322, 2023. https://doi.org/10.32604/iasc.2023.028552



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