A study of deep neural network controller-based power quality improvement of hybrid PV/Wind systems by using smart inverter

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dc.contributor.authorAb-BelKhair, Adel
dc.contributor.authorRahebi, Javad
dc.contributor.authorAbdulhamed Mohamed Nureddin, Abdulbaset
dc.date.accessioned2021-05-15T11:33:20Z
dc.date.available2021-05-15T11:33:20Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractPresently, climate change and global warming are the most uncontrolled global challenges due to the extensive fossil fuel usage for power generation and transportation. Nowadays, most of the developed countries are concentrating on developing alternative resources; consequently, they did huge investments in research and development. In general, alternative energy resources including hydropower, solar power, and wind energy are not harmful to nature. Today, solar power and wind power are very popular alternative energy sources due to their enormous availability in nature. In this paper, the photovoltaic cell and wind energy systems are investigated under various weather conditions. Based on the findings, we developed an advanced intelligent controller system that tracks the maximum power point. The MPPT controller is a must for the renewable energy sources due to unpredictable weather conditions. The main objective of this paper is to propose a new algorithm that is based on deep neural network (DNN) and maximum power point tracking (MPPT), which was simulated in a MATLAB environment for photovoltaic (PV) and wind-based power generation systems. The development of an advanced DNN controller that improves the power quality and reduces THD value for the microgrid integration of hybrid PV/wind energy system was performed. The MATLAB simulation tool has been used to develop the proposed system and tested its performance in different operating situations. Finally, we analyzed the simulation results applying the IEEE 1547 standard.en_US
dc.identifier.doi10.1155/2020/8891469
dc.identifier.issn1110-662X
dc.identifier.issn1687-529X
dc.identifier.scopus2-s2.0-85098513322
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1155/2020/8891469
dc.identifier.urihttps://hdl.handle.net/20.500.12939/134
dc.identifier.volume2020en_US
dc.identifier.wosWOS:000603581000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAb-BelKhair, Adel
dc.institutionauthorRahebi, Javad
dc.institutionauthorAbdulhamed Mohamed Nureddin, Abdulbaset
dc.language.isoen
dc.publisherHindawi Ltden_US
dc.relation.ispartofInternational Journal of Photoenergy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep Neural Network-Based MPPT For PVen_US
dc.titleA study of deep neural network controller-based power quality improvement of hybrid PV/Wind systems by using smart inverter
dc.typeArticle

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