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AuthorKhalid, Haris M.
AuthorFlitti, Farid
AuthorMahmoud, Magdi S.
AuthorHamdan, Mutaz M.
AuthorMuyeen, S.M.
AuthorDong, Zhao Yang
Available date2023-02-26T08:29:56Z
Publication Date2023
Publication NameSustainable Energy, Grids and Networks
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.segan.2023.101009
URIhttp://hdl.handle.net/10576/40356
AbstractModern power grid is a generation mix of conventional generation facilities and variable renewable energy resources (VRES). The complexity of such a power grid with generation mix has routed the utilization of infrastructures involving phasor measurement units (PMUs). This is to have access to real-time grid information. However, the traffic of digital information and communication is potentially vulnerable to data-injection and cyber attacks. To address this issue, a median regression function (MRF)-based state estimation is presented in this paper. The algorithm was stationed at each monitoring node using interacting multiple model (IMM)-based fusion architecture. An exogenous variable-driven representation of the state is considered for the system. A mapping function-based initial regression analysis is made to depict the margins of state estimate in the presence of data-injection. A median regression function is built on top of it while generating and evaluating the residuals. The tests were conducted on a revisited New England 39-Bus system with large scale photovoltaic (PV) power plant. The system was affected with multiple system disturbances and severe data-injection attacks. The results show the effectiveness of the proposed MRF method against the mainstream and regression methods. The proposed scheme can accurately estimate the states and evaluate the contaminated measurements while improving the situation awareness of wide area monitoring systems (WAMS) operations in modern power grids 2023 The Author(s)
SponsorThe publication of this article was funded by Qatar National Library .
Languageen
PublisherElsevier Ltd
SubjectBad-data injection attacks
Cyber security
Cyber-physical systems
Dynamic vulnerability assessments (DVA)
Exogenous
Generation mix
Median filter
Model prediction
Phasor measurement unit (PMU)
Regression
Renewable energy integration (REI)
Situational awareness
State estimation
Synchrophasor
Variable renewable energy resources (VRES)
Wide area monitoring system (WAMS)
TitleWide area monitoring system operations in modern power grids: A median regression function-based state estimation approach towards cyber attacks
TypeArticle
Volume Number34
dc.accessType Open Access


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