skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Controlled Islanding under Complete and Partial False Data Injection Attack Uncertainties against Phasor Measurement Units
The widespread application of phasor measurement units has improved grid operational reliability. However, this has increased the risk of cyber threats such as false data injection attack that mislead time-critical measurements, which may lead to incorrect operator actions. While a single incorrect operator action might not result in a cascading failure, a series of actions impacting critical lines and transformers, combined with pre-existing faults or scheduled maintenance, might lead to widespread outages. To prevent cascading failures, controlled islanding strategies are traditionally implemented. However, islanding is effective only when the received data are trustworthy. This paper investigates two multi-objective controlled islanding strategies to accommodate data uncertainties under scenarios of lack of or partial knowledge of false data injection attacks. When attack information is not available, the optimization problem maximizes island observability using a minimum number of phasor measurement units for a more accurate state estimation. When partial attack information is available, vulnerable phasor measurement units are isolated to a smaller island to minimize the impacts of attacks. Additional objectives ensure steady-state and transient-state stability of the islands. Simulations are performed on 200-bus, 500-bus, and 2000-bus systems.  more » « less
Award ID(s):
1750531
PAR ID:
10464113
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Energies
Volume:
15
Issue:
15
ISSN:
1996-1073
Page Range / eLocation ID:
5723
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. In this paper, we demonstrate the feasibility of smart malware that advances state-of-the-art attacks by (i) indirectly attacking a computing infrastructure through a cyber-physical system (CPS) that manages the environment in which the computing enterprise operates, (ii) disguising its malicious actions as accidental failures, and (iii) self-learning attack strategies from cyber-physical system measurement data. We address all aspects of the malware, including the construction of the self-learning malware and the launch of a failure injection attack. We validate the attacks in a data-driven CPS simulation environment developed as part of this study. 
    more » « less
  2. The bi-directional communication capabilities that emerged into the smart power grid play a critical role in the grid's secure, reliable and efficient operation. Nevertheless, the data communication functionalities introduced to Advanced Metering Infrastructure (AMI) nodes end the grid's isolation, and expose the network into an array of cyber-security threats that jeopardize the grid's stability and availability. For instance, malware amenable to inject false data into the AMI can compromise the grid's state estimation process and lead to catastrophic power outages. In this paper, we explore several statistical spatio-temporal models for efficient diagnosis of false data injection attacks in smart grids. The proposed methods leverage the data co-linearities that naturally arise in the AMI measurements of the electric network to provide forecasts for the network's AMI observations, aiming to quickly detect the presence of “bad data”. We evaluate the proposed approaches with data tampered with stealth attacks compiled via three different attack strategies. Further, we juxtapose them against two other forecasting-aided detection methods appearing in the literature, and discuss the trade-offs of all techniques when employed on real-world power grid data, obtained from a large university campus. 
    more » « less
  3. This paper proposes a framework to optimally employ static VAR compensators (SVCs) within a customized reconfiguration of system topology, leading to remediation of voltage violations caused by false data injection (FDI) cyberattacks targeting smart distribution grids. The designed framework contains formulations associated with planning and operation phases. In the planning phase, the scrutinized system, modified by photovoltaic (PV) units, is enhanced by optimally allocating static VAR compensators (SVCs) to keep the unity power factor throughout the system. Then, distribution system operator (DSO), being in attacker’s shoe, examines relevant cyberattack scenarios leading to voltage violations within the distribution system. Finally, in the operation phase, DSO takes advantage of the optimally planned SVCs to identify proper vectors (i.e., remedial actions) to cope with such potential scenarios of cyberattacks. These (to be recognized) vectors are associated with the variable shunt susceptance of the mentioned SVCs, which will be identified by solving a customized distribution feeder reconfiguration (DFR) problem in the operation phase. The main objective of the customized DFR is to maximize the contributions of SVCs through enhancing the voltage profile of the targeted system. This will enable DSO to mitigate the negative impacts of the FDI attacks and recover the voltage profile of the smart distribution network. The effectiveness of the proposed RAS is validated on three different smart test systems (i.e., 33-bus, 95-bus, and 136-bus systems), which are modified to contain SVC components and renewable-based distributed generation (DG) units. 
    more » « less
  4. null (Ed.)
    Power system state estimation is an important component of the status and healthiness of the underlying electric power grid real-time monitoring. However, such a component is prone to cyber-physical attacks. The majority of research in cyber-physical power systems security focuses on detecting measurements False-Data Injection attacks. While this is important, measurement model parameters are also a most important part of the state estimation process. Measurement model parameters though, also known as static-data, are not monitored in real-life applications. Measurement model solutions ultimately provide estimated states. A state-of-the-art model presents a two-step process towards simultaneous false-data injection security: detection and correction. Detection steps are χ2 statistical hypothesis test based, while correction steps consider the augmented state vector approach. In addition, the correction step uses an iterative solution of a relaxed non-linear model with no guarantee of optimal solution. This paper presents a linear programming method to detect and correct cyber-attacks in the measurement model parameters. The presented bi-level model integrates the detection and correction steps. Temporal and spatio characteristics of the power grid are used to provide an online detection and correction tool for attacks pertaining the parameters of the measurement model. The presented model is implemented on the IEEE 118 bus system. Comparative test results with the state-of-the-art model highlight improved accuracy. An easy-to-implement model, built on the classical weighted least squares solution, without hard-to-derive parameters, highlights potential aspects towards real-life applications. 
    more » « less
  5. null (Ed.)
    Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects. 
    more » « less