Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81243
DC FieldValueLanguage
dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorGanjkhani, M-
dc.creatorFallah, SN-
dc.creatorBadakhshan, S-
dc.creatorShamshirband, S-
dc.creatorChau, KW-
dc.date.accessioned2019-08-23T08:29:53Z-
dc.date.available2019-08-23T08:29:53Z-
dc.identifier.urihttp://hdl.handle.net/10397/81243-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2019 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/).en_US
dc.rightsThe following publication Ganjkhani M, Fallah SN, Badakhshan S, Shamshirband S, Chau K-W. A Novel Detection Algorithm to Identify False Data Injection Attacks on Power System State Estimation. Energies. 2019; 12(11):2209 is available at https://doi.org/10.3390/en12112209en_US
dc.subjectArtificial neural network (ANN)en_US
dc.subjectFalse data injection attack (FDIA)en_US
dc.subjectNonlinear autoregressive exogenous (NARX) bad data detectionen_US
dc.subjectState estimationen_US
dc.titleA novel detection algorithm to identify false data injection attacks on power system state estimationen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume12en_US
dc.identifier.issue11en_US
dc.identifier.doi10.3390/en12112209en_US
dcterms.abstractThis paper provides a novel bad data detection processor to identify false data injection attacks (FDIAs) on the power system state estimation. The attackers are able to alter the result of the state estimation virtually intending to change the result of the state estimation without being detected by the bad data processors. However, using a specific configuration of an artificial neural network (ANN), named nonlinear autoregressive exogenous (NARX), can help to identify the injected bad data in state estimation. Considering the high correlation between power system measurements as well as state variables, the proposed neural network-based approach is feasible to detect any potential FDIAs. Two different strategies of FDIAs have been simulated in power system state estimation using IEEE standard 14-bus test system for evaluating the performance of the proposed method. The results indicate that the proposed bad data detection processor is able to detect the false injected data launched into the system accurately.-
dcterms.bibliographicCitationEnergies, 2019, v. 12, no. 11, 2209-
dcterms.isPartOfEnergies-
dcterms.issued2019-
dc.identifier.isiWOS:000472635900176-
dc.identifier.scopus2-s2.0-85067256282-
dc.identifier.eissn1996-1073en_US
dc.identifier.artn2209en_US
dc.description.validate201908 bcma-
dc.description.oapublished_final-
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