1,327 research outputs found
Vulnerability Assessment of Large-scale Power Systems to False Data Injection Attacks
This paper studies the vulnerability of large-scale power systems to false
data injection (FDI) attacks through their physical consequences. Prior work
has shown that an attacker-defender bi-level linear program (ADBLP) can be used
to determine the worst-case consequences of FDI attacks aiming to maximize the
physical power flow on a target line. This ADBLP can be transformed into a
single-level mixed-integer linear program, but it is hard to solve on large
power systems due to numerical difficulties. In this paper, four
computationally efficient algorithms are presented to solve the attack
optimization problem on large power systems. These algorithms are applied on
the IEEE 118-bus system and the Polish system with 2383 buses to conduct
vulnerability assessments, and they provide feasible attacks that cause line
overflows, as well as upper bounds on the maximal power flow resulting from any
attack.Comment: 6 pages, 5 figure
Load curve data cleansing and imputation via sparsity and low rank
The smart grid vision is to build an intelligent power network with an
unprecedented level of situational awareness and controllability over its
services and infrastructure. This paper advocates statistical inference methods
to robustify power monitoring tasks against the outlier effects owing to faulty
readings and malicious attacks, as well as against missing data due to privacy
concerns and communication errors. In this context, a novel load cleansing and
imputation scheme is developed leveraging the low intrinsic-dimensionality of
spatiotemporal load profiles and the sparse nature of "bad data.'' A robust
estimator based on principal components pursuit (PCP) is adopted, which effects
a twofold sparsity-promoting regularization through an -norm of the
outliers, and the nuclear norm of the nominal load profiles. Upon recasting the
non-separable nuclear norm into a form amenable to decentralized optimization,
a distributed (D-) PCP algorithm is developed to carry out the imputation and
cleansing tasks using networked devices comprising the so-termed advanced
metering infrastructure. If D-PCP converges and a qualification inequality is
satisfied, the novel distributed estimator provably attains the performance of
its centralized PCP counterpart, which has access to all networkwide data.
Computer simulations and tests with real load curve data corroborate the
convergence and effectiveness of the novel D-PCP algorithm.Comment: 8 figures, submitted to IEEE Transactions on Smart Grid - Special
issue on "Optimization methods and algorithms applied to smart grid
Risk Assessment of Stealthy Attacks on Uncertain Control Systems
In this article, we address the problem of risk assessment of stealthy
attacks on uncertain control systems. Considering data injection attacks that
aim at maximizing impact while remaining undetected, we use the recently
proposed output-to-output gain to characterize the risk associated with the
impact of attacks under a limited system knowledge attacker. The risk is
formulated using a well-established risk metric, namely the maximum expected
loss. Under this setups, the risk assessment problem corresponds to an
untractable infinite non-convex optimization problem. To address this
limitation, we adopt the framework of scenario-based optimization to
approximate the infinite non-convex optimization problem by a sampled
non-convex optimization problem. Then, based on the framework of dissipative
system theory and S-procedure, the sampled non-convex risk assessment problem
is formulated as an equivalent convex semi-definite program. Additionally, we
derive the necessary and sufficient conditions for the risk to be bounded.
Finally, we illustrate the results through numerical simulation of a
hydro-turbine power system
Risk-based Security Measure Allocation Against Injection Attacks on Actuators
This article considers the problem of risk-optimal allocation of security
measures when the actuators of an uncertain control system are under attack. We
consider an adversary injecting false data into the actuator channels. The
attack impact is characterized by the maximum performance loss caused by a
stealthy adversary with bounded energy. Since the impact is a random variable,
due to system uncertainty, we use Conditional Value-at-Risk (CVaR) to
characterize the risk associated with the attack. We then consider the problem
of allocating the security measures which minimize the risk. We assume that
there are only a limited number of security measures available. Under this
constraint, we observe that the allocation problem is a mixed-integer
optimization problem. Thus we use relaxation techniques to approximate the
security allocation problem into a Semi-Definite Program (SDP). We also compare
our allocation method across different risk measures: the worst-case
measure, the average (nominal) measure, and across different search
algorithms: the exhaustive and the greedy search algorithms. We depict the
efficacy of our approach through numerical examples.Comment: Submitted to IEEE Open Journal of Control Systems (OJ-CSYS
Evaluating Resilience of Electricity Distribution Networks via A Modification of Generalized Benders Decomposition Method
This paper presents a computational approach to evaluate the resilience of
electricity Distribution Networks (DNs) to cyber-physical failures. In our
model, we consider an attacker who targets multiple DN components to maximize
the loss of the DN operator. We consider two types of operator response: (i)
Coordinated emergency response; (ii) Uncoordinated autonomous disconnects,
which may lead to cascading failures. To evaluate resilience under response
(i), we solve a Bilevel Mixed-Integer Second-Order Cone Program which is
computationally challenging due to mixed-integer variables in the inner problem
and non-convex constraints. Our solution approach is based on the Generalized
Benders Decomposition method, which achieves a reasonable tradeoff between
computational time and solution accuracy. Our approach involves modifying the
Benders cut based on structural insights on power flow over radial DNs. We
evaluate DN resilience under response (ii) by sequentially computing autonomous
component disconnects due to operating bound violations resulting from the
initial attack and the potential cascading failures. Our approach helps
estimate the gain in resilience under response (i), relative to (ii)
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