A Horse Herd Optimization Algorithm (HOA)-Based MPPT Technique under Partial and Complex Partial Shading Conditions

Sajid Sarwar, Muhammad Annas Hafeez, Muhammad Yaqoob Javed, Aamer Bilal Asghar and Krzysztof Ejsmont
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Sajid Sarwar: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan
Muhammad Annas Hafeez: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan
Muhammad Yaqoob Javed: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan
Aamer Bilal Asghar: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan
Krzysztof Ejsmont: Faculty of Mechanical and Industrial Engineering, Warsaw University of Technology, 02-524 Warsaw, Poland

Energies, 2022, vol. 15, issue 5, 1-22

Abstract: The inconsistent irradiance, temperature, and unexpected behavior of the weather affect the output of photovoltaic (PV) systems, classified as partial or complex partial shading conditions. Under these circumstances, obtaining the maximum output power from PV systems becomes problematic. This paper proposes a population-based optimization model, the horse herd optimization algorithm (HOA), inspired by natural behavior, to solicit the maximum power under partial or complex partial shading conditions. It is an intelligent strategy inspired by the surprise pounce-chasing style of the horse herd model. The proposed technique outperforms the standard in different weather conditions, needs less computational time, and has a fast convergence speed and zero oscillations after reaching a power point’s maximum limit. A performance comparison of the HOA is achieved with conventional techniques, such as “perturb and observe” (P&O), the bio-inspired adaptive cuckoo search optimization (ACS), particle swarm optimization (PSO), and the dragonfly algorithm (DA). The following comparison of the presented scheme with the other techniques shows its better performance with respect to fast tracking and efficiency, as well as stability under disparate weather conditions and the ability to obtain maximum power with negligible oscillation under partial and complex shading.

Keywords: photovoltaic (PV); incremental conductance (InC); dragonfly (DA); maximum power point tracking (MPPT); perturb and observe (P&O); adaptive cuckoo search optimization (ACS); particle swarm optimization (PSO); local maxima (LM); complex partial shading (CPS); partial shading (PS) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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