A novel MPPT algorithm based on optimized artificial neural network by using FPSOGSA for standalone photovoltaic energy systems

Basit Öğe Kaydı
dc.contributor.authorDuman, Serhat
dc.contributor.authorYörükeren, Nuran
dc.contributor.authorAltaş, İsmail H.
dc.date.accessioned2020-04-30T22:38:47Z
dc.date.available2020-04-30T22:38:47Z
dc.date.issued2018
dc.departmentDÜ, Teknoloji Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.descriptionDuman, Serhat/0000-0002-1091-125Xen_US
dc.descriptionWOS: 000422933800022en_US
dc.description.abstractMaximum power point tracking (MPPT) algorithms are used to maximize the output power of the photovoltaic (PV) panel under different temperature and irradiance conditions in photovoltaic energy sources (PVES). In this paper, a novel MPPT method based on optimized artificial neural network by using hybrid particle swarm optimization and gravitational search algorithm based on fuzzy logic (FPSOGSA) is proposed to track the operation of the PV panel in maximum power point (MPP). The performance of the proposed MPPT approach is tested by doing the simulation and experimental studies under different environmental conditions. The proposed method is compared with the conventional perturb and observation (P&O) method for standalone PVES. The results of the comparison the obtained from the simulation and experimental studies demonstrate that the proposed MPPT method provides the reduction oscillations around the MPP and the increased maximum power yield of the PV system in the steady state.en_US
dc.description.sponsorshipKocaeli University Scientific Research Projects UnitKocaeli University [2012/067]en_US
dc.description.sponsorshipThis study was supported by Kocaeli University Scientific Research Projects Unit. Project No: 2012/067.en_US
dc.identifier.doi10.1007/s00521-016-2447-9en_US
dc.identifier.endpage278en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage257en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-016-2447-9
dc.identifier.urihttps://hdl.handle.net/20.500.12684/2445
dc.identifier.volume29en_US
dc.identifier.wosWOS:000422933800022en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMPPTen_US
dc.subjectPhotovoltaic energy systemsen_US
dc.subjectArtificial neural networken_US
dc.subjectFPSOGSAen_US
dc.subjectOptimizationen_US
dc.titleA novel MPPT algorithm based on optimized artificial neural network by using FPSOGSA for standalone photovoltaic energy systemsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
2445.pdf
Boyut:
5.72 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Makale Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu