Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/237662
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dc.titleDEFENSE STRATEGIES AGAINST FALSE DATA INJECTION ATTACKS IN THE SMART GRID
dc.contributor.authorSIU JUN YEN
dc.date.accessioned2023-02-28T18:00:35Z
dc.date.available2023-02-28T18:00:35Z
dc.date.issued2022-08-15
dc.identifier.citationSIU JUN YEN (2022-08-15). DEFENSE STRATEGIES AGAINST FALSE DATA INJECTION ATTACKS IN THE SMART GRID. ScholarBank@NUS Repository.
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/237662
dc.description.abstractFalse Data Injection Attack (FDIA) is a sophisticated and advanced class of attack that injects false data to disturb system operation. FDIA can manipulate critical data such as measurements or control signals to develop wrong control actions or operate in a non-optimal condition while bypassing existing protection protocols. Consequently, causing the system to destabilize and incur substantial economic losses before the system operator can take any remedial action. Therefore, this thesis focuses on performing security studies and developing novel defense solutions against FDIA to safeguard and ensure a resilient grid. The control systems and domains of focus for security studies include centralized economic dispatch problem, tap transformers in transmission networks with solar photovoltaics and phasor measurement units, automatic generation control, and distributed control in islanded AC microgrids. In these studies, simulation and experimental results demonstrated the impact of attack and verified the effectiveness of the proposed defense strategies.
dc.language.isoen
dc.subjectFalse Data Injection Attacks, Smart Grid, Defense Strategies, Generation, Transmission, Distribution
dc.typeThesis
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.contributor.supervisorSanjib K Panda
dc.description.degreePh.D
dc.description.degreeconferredDOCTOR OF PHILOSOPHY (CDE-ENG)
dc.identifier.orcid0000-0001-6473-2786
Appears in Collections:Ph.D Theses (Open)

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