Show simple item record

dc.contributor.authorZeng, L
dc.contributor.authorXia, T
dc.contributor.authorElsayed, SK
dc.contributor.authorAhmed, M
dc.contributor.authorRezaei, M
dc.contributor.authorJermsittiparsert, K
dc.contributor.authorDampage, U
dc.contributor.authorMohamed, MA
dc.date.accessioned2021-06-18T04:51:55Z
dc.date.available2021-06-18T04:51:55Z
dc.date.issued2021
dc.identifier.issn2071-1050
dc.identifier.doi10.3390/su13115777
dc.identifier.urihttp://hdl.handle.net/10072/405214
dc.description.abstractA static VAR compensator (SVC) is a critical component for reactive power compensation in electric arc furnaces (EAFs) that is used to relieve the flicker impacts and maintain the voltage level. A weak voltage profile can not only reduce the power-quality services, but can also result in system instability in severe cases. The cybersecurity of EAFs is becoming a significant concern due to their cyber-physical structure. The reliance of SVC controllers on reactive power measurement and network communications has resulted in a cyber-vulnerability point for unauthorized access to the EAF, which can affect its normal operation. This paper addresses concerns about cyber attacks on EAFs, which can cause network communication issues in measurement data for SVCs. Three significant and different types of cyber attacks that are launched on SVC controllers—a replay attack, delay attack, and false data injection attack (FDIA)—were simulated and investigated. In order to stop the activities of cyber attacks, a secured anomaly detection model (ADM) based on a prediction interval is proposed. The proposed model is dependent on a support vector regression and a new smooth cost function for constructing the optimal and symmetrical intervals. A modified algorithm based on teaching–learning-based optimization was developed to adapt the ADM’s parameters during training. The simulation’s outcomes on a genuine dataset showed the strong capability of the proposed model against cyber attacks in EAFs.
dc.description.peerreviewedYes
dc.languageen
dc.publisherMDPI AG
dc.relation.ispartofpagefrom5777
dc.relation.ispartofissue11
dc.relation.ispartofjournalSustainability
dc.relation.ispartofvolume13
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchData structures and algorithms
dc.subject.fieldofresearchElectronics, sensors and digital hardware
dc.subject.fieldofresearchNanotechnology
dc.subject.fieldofresearchBuilt environment and design
dc.subject.fieldofresearchcode4602
dc.subject.fieldofresearchcode461305
dc.subject.fieldofresearchcode4009
dc.subject.fieldofresearchcode4018
dc.subject.fieldofresearchcode33
dc.titleA novel machine learning-based framework for optimal and secure operation of static var compensators in eafs
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationZeng, L; Xia, T; Elsayed, SK; Ahmed, M; Rezaei, M; Jermsittiparsert, K; Dampage, U; Mohamed, MA, A novel machine learning-based framework for optimal and secure operation of static var compensators in eafs, Sustainability, 2021, 13 (11), pp. 5777
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2021-06-18T04:29:51Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorRezaei, Mostafa


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record