Development of a Hybrid Support Vector Machine with Grey Wolf Optimization Algorithm for Detection of the Solar Power Plants Anomalies

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dc.authoriddeif, mohanad/0000-0002-4388-1480
dc.authorid, hani Attar/0000-0001-8028-7918
dc.contributor.authorAhmed, Qais Ibrahim
dc.contributor.authorAttar, Hani
dc.contributor.authorAmer, Ayman
dc.contributor.authorDeif, Mohanad A.
dc.contributor.authorSolyman, Ahmed A. A.
dc.date.accessioned2024-09-11T19:53:11Z
dc.date.available2024-09-11T19:53:11Z
dc.date.issued2023
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractSolar energy utilization in the industry has grown substantially, resulting in heightened recognition of renewable energy sources from power plants and intelligent grid systems. One of the most important challenges in the solar energy field is detecting anomalies in photovoltaic systems. This paper aims to address this by using various machine learning algorithms and regression models to identify internal and external abnormalities in PV components. The goal is to determine which models can most accurately distinguish between normal and abnormal behavior of PV systems. Three different approaches have been investigated for detecting anomalies in solar power plants in India. The first model is based on a physical model, the second on a support vector machine (SVM) regression model, and the third on an SVM classification model. Grey wolf optimizer was used for tuning the hyper model for all models. Our findings will clarify that the SVM classification model is the best model for anomaly identification in solar power plants by classifying inverter states into two categories (normal and fault).en_US
dc.identifier.doi10.3390/systems11050237
dc.identifier.issn2079-8954
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85160029734en_US
dc.identifier.urihttps://doi.org/10.3390/systems11050237
dc.identifier.urihttps://hdl.handle.net/11363/8091
dc.identifier.volume11en_US
dc.identifier.wosWOS:000997601200001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofSystemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240903_Gen_US
dc.subjectsolar energyen_US
dc.subjectintelligent grid systemen_US
dc.subjectpower plant anomaliesen_US
dc.subjectPVen_US
dc.titleDevelopment of a Hybrid Support Vector Machine with Grey Wolf Optimization Algorithm for Detection of the Solar Power Plants Anomaliesen_US
dc.typeArticleen_US

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