A Deep Learning-Based Islanding Detection Approach by Considering the Load Demand of DGs Under Different Grid Conditions

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dc.contributor.authorBayrak, Gökay
dc.contributor.authorYılmaz, Alper
dc.date.accessioned2026-02-12T21:02:52Z
dc.date.available2026-02-12T21:02:52Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIslanding detection is a very important issue in the integration of renewable energy systems with the grid. In recent years, especially artificial intelligence and deep learning-based islanding detection methods have come to the fore in terms of providing reliable power quality. In this study, a deep learning-based islanding detection approach by considering power quality and load demand problems is proposed. It is aimed to effectively detect the islanding condition which occurs as a result of unintentional disconnection of distributed generation (DG) systems from the grid. In the proposed approach, a deep learning-based islanding detection method is developed, taking into account the faults and power quality events occurring on the load side like considering asynchronous motor startup, capacitor switching, etc., conditions that are not possible to easily detect by conventional islanding detection methods. With the developed method, it is seen that the islanding event can be distinguished from the power quality events that occur on the grid, even under noisy signals. In this way, the power quality of the grid is increased and the performance of the DG in dynamic load behavior is developed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
dc.identifier.doi10.1007/978-981-19-6490-9_4
dc.identifier.endpage76
dc.identifier.isbn9789819680023
dc.identifier.isbn9789819542734
dc.identifier.isbn9789819540440
dc.identifier.isbn9789819658473
dc.identifier.isbn9789819600571
dc.identifier.isbn9783032147417
dc.identifier.isbn9789819540488
dc.identifier.isbn9789819644292
dc.identifier.isbn9789819637577
dc.identifier.isbn9789819663392
dc.identifier.issn1876-1100
dc.identifier.scopus2-s2.0-85152239408
dc.identifier.scopusqualityQ4
dc.identifier.startpage61
dc.identifier.urihttps://doi.org/10.1007/978-981-19-6490-9_4
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6607
dc.identifier.volume956
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofLecture Notes in Electrical Engineering
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.snmzKA_Scopus_20260212
dc.subjectArtificial intelligence
dc.subjectDeep learning
dc.subjectDistributed generation
dc.subjectIslanding detection
dc.subjectLoad demand
dc.titleA Deep Learning-Based Islanding Detection Approach by Considering the Load Demand of DGs Under Different Grid Conditions
dc.typeBook Chapter

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