Cyber disruption detection in linear power systems

Khalghani, Mohammad Reza; Khushalani-Solanki, Sarika; Solanki, Jignesh; Sargolzaei, Arman

doi:10.1109/naps.2017.8107341

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…We utilize a combined UIO and LQR controllers formed in two layers to control the microgrid . The first layer estimates the microgrid states and detects the UIs using (19). The second layer decreases the frequency discrepancy using (30)-(32).…”

Section: The Proposed Control Methodsmentioning

confidence: 99%

“…The microgrid model is obtained by augmenting all these modules of VSI inverters, network lines, and loads together as in (19):…”

Section: Complete Microgrid Modelmentioning

confidence: 99%

“…In UIO, the states x(k) = x 1 x 2 T are separated into two groups of states: states corresponding to known inputs, x 1 (k) and states corresponding to unknown inputs, x 2 (k) [19].…”

Section: Design Of Unknown Input Observermentioning

confidence: 99%

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Resilient Networked Control of Inverter-Based Microgrids against False Data Injections

et al. 2022

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Inverter-based energy resource is a fast emerging technology for microgrids. Operation of micorgrids with integration of these resources, especially in an islanded operation mode, is challenging. To effectively capture microgrid dynamics and also control these resources in islanded microgrids, a heavy cyber and communication infrastructure is required. This high reliance of microgrids on cyber interfaces makes these systems prone to cyber-disruptions. Hence, the hierarchical control of microgrids, including primary, secondary, and tertiary control, needs to be developed to operate resiliently. This paper shows the vulnerability of microgrid control in the presence of False Data Injection (FDI) attack, which is one type of cyber-disruption. Then, this paper focuses on designing a resilient secondary control based on Unknown Input Observer (UIO) against FDI. The simulation results show the superior performance of the proposed controller over other standard controllers.

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Section: The Proposed Control Methodsmentioning

confidence: 99%

“…The microgrid model is obtained by augmenting all these modules of VSI inverters, network lines, and loads together as in (19):…”

Section: Complete Microgrid Modelmentioning

confidence: 99%

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Resilient Networked Control of Inverter-Based Microgrids against False Data Injections

et al. 2022

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“…In SUIO, the states x(k) = x 1 x 2 T are decoupled into those corresponding to known inputs, x 1 (k) and unknown inputs, x 2 (k) [134]. Here, x 2 (k) state denotes the frequency deviation ∆f (k) which is affected by the UI.…”

Section: Design Of Stochastic Unknown Input Observermentioning

confidence: 99%

Resilient Stochastic Control Strategies in Cyber-Physical Microgrids

Khalghani¹

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Microgrids can locally fulfill the demand and operate isolated from the main grid to sustain critical services even in case of large-scale outages and cascading power failures. Microgrids are the integration of a large number of distributed energy resources in a decentralized way such that the energy supply reliability and resiliency are enhanced against natural disasters, physical and cyber disruptions. This dissertation focuses on three main challenges regarding technologies emerging in microgrids. 1) According to the fast growing number of Electric Vehicles (EV) deployment, their impacts on microgrids resulted from their uncertain behaviors are new concerns for the microgrid operators. These concerns can be EV charging and discharging schedule and locations of EV parking-lots in the system which are considered and solved using stochastic modeling and optimization algorithms based on Artificial Intelligence (AI) techniques. First, charging and discharging scheduling of EVs is minimized using Particle Swarm Optimization (PSO) Algorithm such that the daily load profile is flattened (peak and off-peak shaving) with considering constraints of State-Of-Charging (SoC). Due to the uncertain nature of daily travelling EVs between residential and administrative areas, chronological stochastic modeling is suggested. Furthermore, a multi-objective optimization based on PSO and fuzzification theory is proposed to find the best location of parking lots for these EV aggregators. Two indices, voltage unbalance and power loss, for locating the EV aggregators are considered. During peak hour, these indices can be more critical for a three-phase distribution system. 2) As the penetration of renewable energies, generally, uncertainties, increase in microgrids, a more dynamic and complex system is emerging that makes frequency control of the islanded microgrid more challenging due to stochastic dynamic encumbrances. These stochastic encumbrances can create oscillatory frequency response, which eventually leads to astray controls and instability even under primary and traditional secondary controllers. This dissertation develops AI-based and analytical based secondary control for the islanded microgrids to compensate for the frequency deviation in the presence of intermittent energy resources and uncertain load changes. Two types of artificial I wish to express my deep and sincere gratitude to my supervisor Prof. Jignesh Solanki for his guidance, support and encouragement in entire my Ph.D. program. Prof. Jignesh Solanki was always there to patiently listen and to give valuable advice. I will not forget his great feedback for enhancing my research presentation skills. Besides my supervisor, I cannot begin to express my thanks to Prof. Sarika Khushalani Solanki for participating in lengthy discussions to perfectly align my research and present my accomplishments. She has been very supportive and sympathetic in both my professional and daily lives. Working under these two great professors was a golden opportunity for me to leverage a...

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