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dc.contributor.advisorHovd, Morten
dc.contributor.advisorUhlen, Kjetil
dc.contributor.authorAbooshahab, Mohammad Ali
dc.date.accessioned2022-02-17T11:14:52Z
dc.date.available2022-02-17T11:14:52Z
dc.date.issued2021
dc.identifier.isbn978-82-326-6299-9
dc.identifier.issn2703-8084
dc.identifier.urihttps://hdl.handle.net/11250/2979645
dc.description.abstractIncreased electrification of society, as well as increased incidence of distributed power generation, causes more dynamic operation of modern electrical power grids. Methods for monitoring of power grids have traditionally focused on static operation but must in the future be expected to have to account for system dynamics. On the other hand, privacy concerns for individuals, commercial secrecy concerns for companies, and cost concerns for power system operators, may cause power systems to be only partly known and without sufficient sensors for traditional dynamic state estimation in power systems. This thesis contributes to the understanding of Simultaneous Input and State Estimation (SISE), in particular its stability properties, and develops methods for dynamic monitoring of partially known power systems based on SISE. The other main focus of the thesis is decentralized state estimation. Both the sheer scale of power networks, as well as the fact that different sections of the system may be operated by different companies, make a single centralized estimation center impractical and/or impossible. The thesis therefore develops decentralized estimation approaches for power systems, in which the observability requirements of existing decentralized estimation methods are relaxed. In addition, the thesis also addresses cybersecurity and fault detection as well as sensor placement for power systems and shows how SISE can be exploited in power system stabilizer (PSS) design.
dc.language.isoengen_US
dc.publisherNTNUen_US
dc.relation.ispartofseriesDoctoral theses at NTNU;2021:273
dc.relation.haspartPaper 1: Bitmead, Robert R.; Hovd, Morten; Abooshahab, Mohammad Ali. A Kalman-filtering derivation of simultaneous input and state estimation. Automatica 2019 ;Volum 108. https://doi.org/10.1016/j.automatica.2019.06.030
dc.relation.haspartPaper 2: Abooshahab, Mohammad Ali; Alyaseen, Mohammed M.J.; Bitmead, Robert R.; Hovd, Morten. Simultaneous input & state estimation, singular filtering and stability. - The final published version is available in Automatica 2021 ;Volum 137. https://doi.org/10.1016/j.automatica.2021.110017
dc.relation.haspartPaper 3: Abooshahab, Mohammad Ali; Hovd, Morten; Bitmead, Robert R.. Disturbance and State Estimation in Partially Known Power Networks. I: Proceedings of the IEEE 2019 Conference on Control Technology and Applications. https://doi.org/10.1109/CCTA.2019.8920575
dc.relation.haspartPaper 4: Abooshahab, Mohammad Ali; Hovd, Morten; Bitmead, Robert R. Monitoring Disturbances and States in Partially Known Power Systems
dc.relation.haspartPaper 5: Abooshahab, Mohammad Ali; Hovd, Morten. Distributed H_∞ Filtering for Linear and Nonlinear Systems. I: 2021 IEEE Conference on Control Technology and Applications (CCTA). IEEE conference proceedings 2021 ISBN 978-1-6654-3643-4. s. 685-692 https://doi.org/10.1109/CCTA48906.2021.9659051
dc.relation.haspartPaper 6: Abooshahab, Mohammad Ali; Hovd, Morten. Multi-Rate Distributed Unscented Kalman Filter- ing with Application to Power System Monitoring
dc.relation.haspartPaper 7: Abooshahab, Mohammad Ali; Hovd, Morten; Brekke, Edmund Førland; Song, Xianfeng. A Covariance Consistent Data Fusion method for Power Networks with Multirate Sensors. I: 2020 IEEE Conference on Control Technology and Applications (CCTA). IEEE conference proceedings 2020 ISBN 978-1-7281-7140-1. s. 807-814 https://doi.org/10.1109/CCTA41146.2020.9206382
dc.titleDynamic State Estimation for Electrical Power Gridsen_US
dc.typeDoctoral thesisen_US
dc.subject.nsiVDP::Technology: 500::Information and communication technology: 550::Technical cybernetics: 553en_US
dc.description.localcodeFulltext is not availableen_US


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