Cyber-Attack Detection and Countermeasure for Distributed Electric Springs for Smart Grid Applications (original) (raw)
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In this paper, online detection of false data injection (FDI) attacks and denial of service (DoS) attacks in the smart grid is studied. The system is modelled as a discrete-time linear dynamic system and state estimation is performed using the Kalman filter. The generalized CUSUM algorithm is employed for quickest detection of the cyber-attacks. Detectors are proposed in both centralized and distributed settings. The proposed detectors are robust to time-varying states, attacks, and set of attacked meters. Online estimates of the unknown attack variables are provided, that can be crucial for a quick system recovery. In the distributed setting, due to bandwidth constraints, local centers can only transmit quantized messages to the global center, and a novel event-based sampling scheme called level-crossing sampling with hysteresis (LCSH) is proposed that is shown to exhibit significant advantages compared with the conventional uniform-in-time sampling (US) scheme. Moreover, a distributed dynamic state estimator is proposed based on information filters. Numerical examples illustrate the fast and accurate response of the proposed detectors in detecting both structured and random attacks and their advantages over the existing methods.
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Although distributed control in microgrids is wellknown for reliability and scalability, the absence of a global monitoring entity makes it highly vulnerable to cyber attacks. Considering that the detection of cyber attacks becomes fairly easy for distributed observers, a well-planned set of balanced attacks, commonly termed as stealth attack, can always bypass these observers with the control objectives being successfully met. In this letter, a mitigation technique is thus introduced to remove stealth attack on the frequency control input in AC microgrids. The mitigation is carried out using a novel eventdriven attack-resilient controller for N cooperative grid-forming converters (GfCs), which guarantees resilient synchronization for up to N −1 attacked units. Finally, the resilience capabilities and robustness of the proposed controller are discussed and verified under various scenarios.
IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, 2019
In this paper, we present a novel distributed state estimation approach in networked DC microgrids to detect the false data injection in the microgrid control network. Each microgrid monitored by a distributed state estimator will detect if there is manipulated data received from their neighboring microgrids for control purposes. A dynamic model supporting the dynamic state estimation will be constructed for the networked microgrids. The optimal distributed state estimation, which is robust to load disturbances but sensitive to false data injected from neighboring microgrids will be presented. To demonstrate the effectiveness of the proposed approach, we simulate a 12kV three-bus networked DC microgrids in MATLAB/Simulink. Residual information corresponding to the false data injected from neighbors validates the efficacy of the proposed approach in detecting compromised agents of neighboring microgrids.
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Most of the government, industry, and academic efforts for protecting the power grid against computer attacks have focused on information security mechanisms for preventing and detecting attacks. In this article we give a short introduction to control problems in the power grid and show that in addition to information security mechanisms, we can use control engineering to help improve our analysis and design of an attack-resilient power grid.
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The spread of microgrids is one of the most promising recent trends in the field of power systems, as they can help integrate distributed power sources and other cutting-edge technological advancements into power systems. In order to facilitate their expansion, more research on microgrid protection is needed, as they pose serious challenges to or even totally invalidate traditional protection schemes. In this paper, a comprehensive protection scheme for microgrids is proposed. Our scheme is based on an already extensively researched framework based on dynamic state estimation (DSE) and aims to utilize it for microgrid protection. The individual protection zones of a microgrid are monitored by settingless relays which continuously receive measurements and perform DSE in the time domain to detect abnormalities. However, power faults are not the only root cause of abnormal measurements. Relays can also receive erroneous measurements due to hidden failures in the system or due to malicious actors that try to inject false measurements. For this reason, we add a centralized layer to our scheme. This layer receives measurements from all the settingless relays of the microgrid and uses DSE in the quasi-dynamic domain to determine whether a settingless relay detects an abnormality due to a fault or due to a reason that does not warrant tripping action, which allows us to block erroneous tripping actions. Therefore, our layered approach increases the security and dependability of microgrid protection compared to traditional protection schemes.
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Smart grid is a large complex network with a myriad of vulnerabilities, usually operated in adversarial settings and regulated based on estimated system states. In this study, we propose a novel highly secure distributed dynamic state estimation mechanism for wide-area (multi-area) smart grids, composed of geographically separated subregions, each supervised by a local control center. We firstly propose a distributed state estimator assuming regular system operation, that achieves near-optimal performance based on the local Kalman filters and with the exchange of necessary information between local centers. To enhance the security, we further propose to (i) protect the network database and the network communication channels against attacks and data manipulations via a blockchain (BC)based system design, where the BC operates on the peer-to-peer network of local centers, (ii) locally detect the measurement anomalies in real-time to eliminate their effects on the state estimation process, and (iii) detect misbehaving (hacked/faulty) local centers in real-time via a distributed trust management scheme over the network. We provide theoretical guarantees regarding the false alarm rates of the proposed detection schemes, where the false alarms can be easily controlled. Numerical studies illustrate that the proposed mechanism offers reliable state estimation under regular system operation, timely and accurate detection of anomalies, and good state recovery performance in case of anomalies.
Model-Free Detection of Cyberattacks on Voltage Control in Distribution Grids
2019 15th European Dependable Computing Conference (EDCC)
Incorporating information and communication technology in the operation of the electricity grid is undoubtedly contributing to a more cost-efficient, controllable, and flexible power grid. Although this technology is promoting flexibility and convenience, its integration with the electricity grid is rendering this critical infrastructure inherently vulnerable to cyberattacks that have potential to cause large-scale and farreaching damage. In light of the growing need for a resilient smart grid, developing suitable security mechanisms has become a pressing matter. In this work, we investigate the effectiveness of a model-free state-of-the-art attack-detection method recently proposed by the cybersecurity community in detecting common types of cyberattacks on voltage control in distribution grids. Experimental results show that, by monitoring raw controller and smart-meter data in real time, it is possible to detect denial of service, replay, and integrity attacks, thus contributing to a resilient and more secure grid.