Enhancing State Estimation Accuracy in Power Systems: An ANN-Based Data Mining Approach Defending Cyber Attacks

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dc.authorscopusid57818691400en_US
dc.authorscopusid35103037800en_US
dc.authorscopusid8307948200en_US
dc.contributor.authorAndic, C.
dc.contributor.authorOzturk, A.
dc.contributor.authorTurkay, B.
dc.date.accessioned2024-08-23T16:07:33Z
dc.date.available2024-08-23T16:07:33Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description14th International Conference on Electrical and Electronics Engineering, ELECO 2023 -- 30 November 2023 through 2 December 2023 -- Virtual, Bursa -- 197135en_US
dc.description.abstractIn energy systems, measurement accuracy is jeopardized by bad data arising from cyber attacks. When bad data is detected in the measurement dataset as a result of cyber attacks, it's essential to identify and eliminate these data. However, this elimination process introduces the problem of missing measurement data, threatening the system's observability conditions. This study proposes a data mining approach supported by artificial neural networks to address the missing measurement data issue when bad data is detected. Our proposed method aims to maintain the system's observability by completing the measurement data lost due to bad data. Consequently, the measurement set purified from bad data enhances the accuracy of the crow search algorithm based state estimation results. This methodology has been shown to successfully mitigate the adverse effects of unforeseen situations, such as cyber attacks. © 2023 IEEE.en_US
dc.identifier.doi10.1109/ELECO60389.2023.10416015
dc.identifier.isbn979-835036049-3en_US
dc.identifier.scopus2-s2.0-85185833835en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ELECO60389.2023.10416015
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14728
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectANNen_US
dc.subjectcrow search algorithmen_US
dc.subjectcyber attacksen_US
dc.subjectstate estimationen_US
dc.subjectComputer crimeen_US
dc.subjectCrimeen_US
dc.subjectCyber attacksen_US
dc.subjectData miningen_US
dc.subjectLearning algorithmsen_US
dc.subjectNetwork securityen_US
dc.subjectNeural networksen_US
dc.subjectObservabilityen_US
dc.subjectANNen_US
dc.subjectBad dataen_US
dc.subjectCrow search algorithmen_US
dc.subjectCyber-attacksen_US
dc.subjectEnergy systemsen_US
dc.subjectMeasurement dataen_US
dc.subjectMissing measurementsen_US
dc.subjectPoweren_US
dc.subjectSearch Algorithmsen_US
dc.subjectSystem observabilityen_US
dc.subjectState estimationen_US
dc.titleEnhancing State Estimation Accuracy in Power Systems: An ANN-Based Data Mining Approach Defending Cyber Attacksen_US
dc.typeConference Objecten_US

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