A manhattan metric based perturb and observe maximum power point tracking algorithm for photovoltaic systems

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dc.authoridKESILMIS, ZEHAN/0000-0002-5781-9450
dc.contributor.authorKesilmis, Zehan
dc.date.accessioned2025-01-06T17:37:30Z
dc.date.available2025-01-06T17:37:30Z
dc.date.issued2022
dc.description.abstractMaximum power point tracking (MPPT) requires the use of fast and efficient algorithms that can efficiently discover maximum power point (MPP) even under rapidly changing atmospheric conditions. The conventional perturb and observe (P&O) is a commonly utilized MPPT algorithm due to its parameter-independent and straightforward nature. Despite these merits, P&O suffers from low tracking efficiency, power fluctuations around the MPP, and drift. Various adaptive P&O algorithms have been proposed to reduce these drawbacks. In this paper, a novel Manhattan distance-metric-based adaptive P&O (MPO) algorithm is proposed to deal with problems of the P&O algorithm. The MPO algorithm excels with increasing the MPP convergence rate, reducing the convergence time, and decreasing the power fluctuations. The proposed method has been validated using PSIM simulations and experimental studies under constant and rapidly changing irradiance conditions. Experimental verifications were carried out using the experimental setup containing a step-down converter and ATmega328p microcontroller. In these experiments, the success of the MPO algorithm is compared with P&O and adaptive P&O algorithms. The results show that the MPO algorithm successfully tracks the MPP in steady and rapidly changing irradiance conditions when others fail occasionally. On top of that, over 99% MPPT efficiency is achieved, and the convergence time is also improved.
dc.description.sponsorshipResearch Fund of the Adana Alparslan Turkes Science and Technology University [21103001]
dc.description.sponsorshipThis work was supported by the Research Fund of the Adana Alparslan Turkes Science and Technology University [21103001].
dc.identifier.doi10.1080/15567036.2022.2046662
dc.identifier.endpage492
dc.identifier.issn1556-7036
dc.identifier.issn1556-7230
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85127032324
dc.identifier.scopusqualityQ1
dc.identifier.startpage469
dc.identifier.urihttps://doi.org/10.1080/15567036.2022.2046662
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2244
dc.identifier.volume44
dc.identifier.wosWOS:000766917800001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Inc
dc.relation.ispartofEnergy Sources Part A-Recovery Utilization and Environmental Effects
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectDistance metric
dc.subjectdrift
dc.subjectmanhattan distance
dc.subjectmaximum power point tracking
dc.subjectphotovoltaic
dc.subjectsolar energy harvesting
dc.titleA manhattan metric based perturb and observe maximum power point tracking algorithm for photovoltaic systems
dc.typeArticle

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