Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Modern Optimization Algorithm for Improved Performance of Maximum Power Point Tracker of Partially Shaded PV Systems

Version 1 : Received: 29 May 2023 / Approved: 2 June 2023 / Online: 2 June 2023 (08:48:16 CEST)

A peer-reviewed article of this Preprint also exists.

Eltamaly, A.M.; Almutairi, Z.A.; Abdelhamid, M.A. Modern Optimization Algorithm for Improved Performance of Maximum Power Point Tracker of Partially Shaded PV Systems. Energies 2023, 16, 5228. Eltamaly, A.M.; Almutairi, Z.A.; Abdelhamid, M.A. Modern Optimization Algorithm for Improved Performance of Maximum Power Point Tracker of Partially Shaded PV Systems. Energies 2023, 16, 5228.

Abstract

Because of the rapid advancement in the use of photovoltaic (PV) energy systems, it has become critical to look for ways to improve the energy generated by them. The extracted power from the PV modules is proportional to the output voltage. The relationship between output power and array voltage has only one peak under uniform irradiance, whereas it has multiple peaks under partial shade circumstances (PSC). There is only one global peak (GP) and many local peaks (LPs), where the typical maximum power point trackers (MPPT) may become locked in one of the LPs, significantly reducing the PV system's generated power and efficiency. The metaheuristic optimization algorithms (MOAs) solved this problem, albeit at the expense of the convergence time, which is one of these algorithms' key shortcomings. Most MOAs attempt to lower the convergence time at the cost of the failure rate and the accuracy of the findings because these two factors are interdependent. To address these issues, this work introduces the dandelion optimization algorithm (DOA), a novel optimization algorithm. The DOA's convergence time and failure rate are compared to other modern MOAs in critical scenarios of partial shade PV systems to demonstrate the DOA's superiority. The results obtained from this study showed substantial performance improvement compared to other MOAs, where the convergence time is reduced to 0.4 s with zero failure rate compared to 0.9 s, 1.25 s, and 0.43 s for other MOAs under study. The optimal number of search agents in the swarm, optimal initialization of search agents, and optimal design of the dc-dc converter is introduced for optimal MPPT performance.

Keywords

Photovoltaic; MPPT; partial shading conditions; convergence time; failure rate; metaheuristic; dandelion optimization algorithm (DOA)

Subject

Engineering, Electrical and Electronic Engineering

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