Cyber-Physical Attacks Detection and Resilience Methods in Smart Grids

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dc.contributor.advisorFarag, Hany E. Z.
dc.contributor.authorSawas, Abdullah
dc.date.accessioned2023-12-08T14:21:51Z
dc.date.available2023-12-08T14:21:51Z
dc.date.issued2023-12-08
dc.date.updated2023-12-08T14:21:50Z
dc.degree.disciplineComputer Science
dc.degree.levelDoctoral
dc.degree.namePhD - Doctor of Philosophy
dc.description.abstractBacked by the deployment of increasingly reliable Information and Communication Technologies (ICT) infrastructure, modern power systems heavily depend on computerized circuits to function within an interconnected environment. In particular, Smart Grids (SGs) core domain relies on ICTs networks and components to communicate control signals and data measurements to improve the efficiency of power generation and distribution while maintaining safe and reliable operations. ICTs have also extended the SG domain of interaction to include other utilities, such as the natural gas grid to efficiently utilize multiple energy forms and resources. In the consumer domain, a growing number of appliances and autonomous smart loads equipped with Internet of Things (IoT) technology are being deployed into SG, the results in large portions of electric demand being remotely controlled. Despite their advantages, ICTs are vulnerable to cyber–attacks that can deteriorate SGs' operational safety and integrity. Thus, new approaches to enhance the resiliency of SGs against cyber-physical attacks are needed. To that extent, this thesis develops new resiliency investigation approaches under the three aforementioned domains. First, in the SG domain, an efficient False Data Injection Attack (FDIA) approach is developed imitating an intelligent adversary behavior searching for an optimal attack vector against State Estimation (SE) modules. Simulation results show that using this approach, an adversary can identify attack vectors with minimal size and superior flexibility to manipulate, in real-time, power flow measurements of the system lines as perceived by the SE without the need to acquire additional measurements. Hence, attacks constructed using this approach require less computational time and resources compared to the existing methods making it beneficial for the analysis of cyber–security vulnerabilities and the design of resilient SE modules. Second, under the Integrated Energy System (IES) domain, an operational framework model is developed to be used as a testbed for performing and analyzing the impact of cyber–attacks. The framework models steady–state power and gas flow operations, and presents a new financial interdependency operation scheduling model. The framework is validated on standard power distribution and transmission systems with variable generation and demand scenarios and high renewable penetration levels. Using this framework, an attack resiliency method is developed based on signal processing and machine-learning tools. The method is able to detect 98.6% and 94.5% of the external signals and internal control commands respectively. Third, the vulnerability of Power Distribution Systems (PDSs) to compromised collections of IoT-enabled appliances is investigated, and a stealthy attack strategy is presented. Accordingly, a new index is developed, referred to as Feeder Loading Abnormal Power Spectrum (FLAPS), and used in a novel real-time detection and prediction approach to counter stealthy attacks and estimate the attack onset time. Results demonstrate that the method is able to detect and alert for stealthy attacks in a timely manner, thereby enabling the system to operate reliably and securely. By identifying new attacks, and proposing detection methods and countermeasures, this thesis contributes to the collective efforts to address the risks associated with cyber–attacks against the SGs components. Specifically, the quantitative results show that deploying the proposed methods will enhance the resiliency of SE and IESs, and protect the PDSs against threats of large-scale deployment of IoT-enabled appliances.
dc.identifier.urihttps://hdl.handle.net/10315/41595
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectElectrical engineering
dc.subjectComputer science
dc.subjectArtificial intelligence
dc.subject.keywordsCyber-physical security
dc.subject.keywordsFalse data injection attacks
dc.subject.keywordsState estimation
dc.subject.keywordsPower systems
dc.subject.keywordsIoT appliances
dc.subject.keywordsDistribution systems
dc.subject.keywordsPower and gas integration
dc.subject.keywordsCyber-attacks
dc.subject.keywordsResiliency
dc.subject.keywordsFeeder loading abnormal power spectrum
dc.titleCyber-Physical Attacks Detection and Resilience Methods in Smart Grids
dc.typeElectronic Thesis or Dissertation

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