| 1. |
- Jin, Ming, et al.
(författare)
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A Semidefinite Programming Relaxation under False Data Injection Attacks against Power Grid AC State Estimation
- 2017
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Ingår i: 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538632666 ; , s. 236-243
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Konferensbidrag (refereegranskat)abstract
- The integration of sensing and information technology renders the power grid susceptible to cyber-attacks. To understand how vulnerable the state estimator is, we study its behavior under the worst attacks possible. A general false data injection attack (FDIA) based on the AC model is formulated, where the attacker manipulates sensor measurements to mislead the system operator to make decisions based on a falsified state. To stage such an attack, the optimization problem incorporates constraints of limited resources (allowing only a limited number of measurements to be altered), and stealth operation (ensuring the cyber hack cannot be identified by the bad data detection algorithm). Due to the nonlinear AC power flow model and combinatorial selection of compromised sensors, the problem is nonconvex and cannot be solved in polynomial time; however, it is shown that convexification of the original problem based on a semidefinite programming (SDP) relaxation and a sparsity penalty is able to recover a near-optimal solution. This represents the first study to solve the AC-based FDIA. Simulations on a 30-bus system illustrate that the proposed attack requires only sparse sensor manipulation and remains stealthy from the residual-based bad data detection mechanism. In light of the analysis, this study raises new challenges on grid defense mechanism and attack detection strategy.
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| 2. |
- Jin, Ming, et al.
(författare)
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Power Grid AC-Based State Estimation : Vulnerability Analysis Against Cyber Attacks
- 2019
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Ingår i: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286 .- 1558-2523. ; 64:5, s. 1784-1799
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Tidskriftsartikel (refereegranskat)abstract
- To ensure grid efficiency and reliability, power system operators continuously monitor the operational characteristics of the grid through a critical process called state estimation (SE), which performs the task by filtering and fusing various measurements collected from grid sensors. This study analyzes the vulnerability of the key operation module, namely ac-based SE, against potential cyber attacks on data integrity, also known as false data injection attack (FDIA). A general form of FDIA can be formulated as an optimization problem, whose objective is to find a stealthy and sparse data injection vector on the sensor measurements with the aim of making the state estimate spurious and misleading. Due to the nonlinear ac measurement model and the cardinality constraint, the problem includes both continuous and discrete nonlinearities. To solve the FDIA problem efficiently, we propose a novel convexification framework based on semidefinite programming (SDP). By analyzing a globally optimal SDP solution, we delineate the "attackable region" for any given set of measurement types and grid topology, where the spurious state can be falsified by FDIA. Furthermore, we prove that the attack is stealthy and sparse, and derive performance bounds. Simulation results on various IEEE test cases indicate the efficacy of the proposed convexification approach. From the grid protection point of view, the results of this study can be used to design a security metric for the current practice against cyber attacks, redesign the bad data detection scheme, and inform proposals of grid hardening. From a theoretical point of view, the proposed framework can be used for other nonconvex problems in power systems and beyond.
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| 3. |
- Lavaei, Javad, et al.
(författare)
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Power flow optimization using positive quadratic programming
- 2011
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Konferensbidrag (refereegranskat)abstract
- The problem to minimize power losses in an electrical network subject to voltage and power constraints is in general hard to solve. However, it has recently been discovered that semidefinite programming relaxations in many cases enable exact computation of the global optimum. Here we point out a fundamental reason for the successful relaxations, namely that the passive network components give rise to matrices with nonnegative off-diagonal entries. Recent progress on quadratic programming with Metzler matrix structure can therefore be applied.
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| 4. |
- Molzahn, Daniel K., et al.
(författare)
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A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems
- 2017
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Ingår i: IEEE Transactions on Smart Grid. - : Institute of Electrical and Electronics Engineers (IEEE). - 1949-3053 .- 1949-3061. ; 8:6, s. 2941-2962
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Tidskriftsartikel (refereegranskat)abstract
- Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. This paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.
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