Enhanced ANN-Based MPPT for Photovoltaic Systems: Integrating Metaheuristic and Analytical Algorithms for Optimal Performance Under Partial Shading

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dc.authorid0000-0002-6402-0479
dc.authorid0000-0002-1038-7224
dc.authorid0000-0002-4411-411X
dc.authorid0000-0003-1663-713X
dc.authorid0000-0002-2073-8956
dc.contributor.authorDemirci, Alpaslan
dc.contributor.authorDagal, Idriss
dc.contributor.authorTercan, Said Mirza
dc.contributor.authorGundogdu, Hasan
dc.contributor.authorTerkes, Musa
dc.contributor.authorCali, Umit
dc.date.accessioned2026-01-31T15:08:39Z
dc.date.available2026-01-31T15:08:39Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractThe efficiency of photovoltaic (PV) systems significantly decreases under partial shading conditions (PSC), leading to challenges in accurately tracking the maximum power point (MPP). This paper presents an enhanced Artificial Neural Network (ANN) to improve the performance of MPP tracking (MPPT) in PV systems subject to PSC. The proposed algorithm is based on an advanced ANN model trained with widely known analytical and metaheuristic algorithms, providing higher accuracy and faster convergence than existing methods. Furthermore, the ANN model was developed and trained using an extensive dataset that includes diverse shading scenarios, irradiation levels, and temperature conditions, with metaheuristic algorithms playing a key role in enhancing its training process. The performance of the proposed system has been evaluated through extensive simulations and sensitivity analyses. The results demonstrate that the improved ANN-based MPPT algorithm consistently outperforms existing MPPT techniques, including the Perturb and Observe (P&O) and Grey Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and Particle Swarm Optimization (PSO) methods. The proposed approach achieves higher efficiency, faster response times, and improved stability under dynamic shading conditions. Specifically, its superior efficiency reaches up to 99.98% under clear sky conditions (CSC) and up to 99.97% under PSC, as verified through extensive simulations using MPPT efficiency metrics. This advancement holds significant potential for optimizing the energy yield of PV systems, promoting more reliable and efficient renewable energy solutions, especially when operating in challenging environmental conditions.
dc.identifier.doi10.1109/ACCESS.2025.3572554
dc.identifier.endpage92799
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-105006714589
dc.identifier.scopusqualityQ1
dc.identifier.startpage92783
dc.identifier.urihttps://doi.org./10.1109/ACCESS.2025.3572554
dc.identifier.urihttps://hdl.handle.net/20.500.12662/10716
dc.identifier.volume13
dc.identifier.wosWOS:001499585600020
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260128
dc.subjectConvergence
dc.subjectMetaheuristics
dc.subjectAccuracy
dc.subjectHeuristic algorithms
dc.subjectNeurons
dc.subjectArtificial neural networks
dc.subjectOscillators
dc.subjectMicrogrids
dc.subjectRenewable energy sources
dc.subjectArtificial neural network
dc.subjectmaximum power point tracking
dc.subjectmetaheuristic algorithms
dc.subjectpartial shading
dc.subjectphotovoltaic
dc.subjectphotovoltaic
dc.titleEnhanced ANN-Based MPPT for Photovoltaic Systems: Integrating Metaheuristic and Analytical Algorithms for Optimal Performance Under Partial Shading
dc.typeArticle

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