Detection of topology errors by state estimation (power systems)
TL;DR: In this paper, the use of normalized residuals that result from state estimation is proposed for the detection of topology errors, including line or transformer outage, bus split, and shunt capacitor/reactor switching.
Abstract: Errors in the telemetered data of breaker and switch status, through the network topology processor in the EMS (energy management system) computer, may result in errors in the determination of the current network topology of the system. The use of normalized residuals that result from state estimation is proposed for the detection of topology errors. Three types of topology errors are considered: line or transformer outage, bus split, and shunt capacitor/reactor switching. Conditions for detectability of topology errors are presented. The conditions are tested on the IEEE 30 bus system, and the results confirm the theoretical predictions. The problem of topology error identification is also discussed. >
Citations
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TL;DR: In this article, a new class of attacks, called false data injection attacks, against state estimation in electric power grids is presented and analyzed, under the assumption that the attacker can access the current power system configuration information and manipulate the measurements of meters at physically protected locations such as substations.
Abstract: A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including interacting bad measurements introduced by arbitrary, nonrandom causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.In this article, we expose an unknown vulnerability of existing bad measurement detection algorithms by presenting and analyzing a new class of attacks, called false data injection attacks, against state estimation in electric power grids. Under the assumption that the attacker can access the current power system configuration information and manipulate the measurements of meters at physically protected locations such as substations, such attacks can introduce arbitrary errors into certain state variables without being detected by existing algorithms. Moreover, we look at two scenarios, where the attacker is either constrained to specific meters or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios to change the results of state estimation in arbitrary ways. We also extend these attacks to generalized false data injection attacks, which can further increase the impact by exploiting measurement errors typically tolerated in state estimation. We demonstrate the success of these attacks through simulation using IEEE test systems, and also discuss the practicality of these attacks and the real-world constraints that limit their effectiveness.
2,064 citations
TL;DR: This is the first work of its kind, which quantitatively analyzes the damage of the false data injection attacks to power system operation and security, and provides an in-depth insight on effective attack prevention with limited protection resource budget.
Abstract: State estimation is a key element in today's power systems for reliable system operation and control. State estimation collects information from a large number of meter measurements and analyzes it in a centralized manner at the control center. Existing state estimation approaches were traditionally assumed to be able to tolerate and detect random bad measurements. They were, however, recently shown to be vulnerable to intentional false data injection attacks. This paper fully develops the concept of load redistribution (LR) attacks, a special type of false data injection attacks, and analyzes their damage to power system operation in different time steps with different attacking resource limitations. Based on damaging effect analysis, we differentiate two attacking goals from the adversary's perspective, i.e., immediate attacking goal and delayed attacking goal. For the immediate attacking goal, this paper identifies the most damaging LR attack through a max-min attacker-defender model. Then, the criterion of determining effective protection strategies is explained. The effectiveness of the proposed model is tested on a 14-bus system. To the author's best knowledge, this is the first work of its kind, which quantitatively analyzes the damage of the false data injection attacks to power system operation and security. Our analysis hence provides an in-depth insight on effective attack prevention with limited protection resource budget.
453 citations
04 Nov 2010
TL;DR: This work proposes two algorithms to place encrypted devices in the system such as to maximize their utility in terms of increased system security, and illustrates the effectiveness of these algorithms on two IEEE benchmark power networks under two attack and protection cost models.
Abstract: State estimators in power systems are currently used to, for example, detect faulty equipment and to route power flows. It is believed that state estimators will also play an increasingly important role in future smart power grids, as a tool to optimally and more dynamically route power flows. Therefore security of the estimator becomes an important issue. The estimators are currently located in control centers, and large numbers of measurements are sent over unencrypted communication channels to the centers. We here study stealthy false-data attacks against these estimators. We define a security measure tailored to quantify how hard attacks are to perform, and describe an efficient algorithm to compute it. Since there are so many measurement devices in these systems, it is not reasonable to assume that all devices can be made encrypted overnight in the future. Therefore we propose two algorithms to place encrypted devices in the system such as to maximize their utility in terms of increased system security. We illustrate the effectiveness of our algorithms on two IEEE benchmark power networks under two attack and protection cost models.
419 citations
TL;DR: It is shown how normal operations of power networks can be statistically distinguished from the case under stealthy attacks, and two machine-learning-based techniques for stealthy attack detection are proposed.
Abstract: Aging power industries, together with the increase in demand from industrial and residential customers, are the main incentive for policy makers to define a road map to the next-generation power system called the smart grid. In the smart grid, the overall monitoring costs will be decreased, but at the same time, the risk of cyber attacks might be increased. Recently, a new type of attacks (called the stealth attack) has been introduced, which cannot be detected by the traditional bad data detection using state estimation. In this paper, we show how normal operations of power networks can be statistically distinguished from the case under stealthy attacks. We propose two machine-learning-based techniques for stealthy attack detection. The first method utilizes supervised learning over labeled data and trains a distributed support vector machine (SVM). The design of the distributed SVM is based on the alternating direction method of multipliers, which offers provable optimality and convergence rate. The second method requires no training data and detects the deviation in measurements. In both methods, principal component analysis is used to reduce the dimensionality of the data to be processed, which leads to lower computation complexities. The results of the proposed detection methods on IEEE standard test systems demonstrate the effectiveness of both schemes.
363 citations
04 Nov 2010
TL;DR: The problem of constructing malicious data attack of smart grid state estimation is considered together with countermeasures that detect the presence of such attacks and an efficient algorithm with polynomial-time complexity is obtained.
Abstract: The problem of constructing malicious data attack of smart grid state estimation is considered together with countermeasures that detect the presence of such attacks. For the adversary, using a graph theoretic approach, an efficient algorithm with polynomial-time complexity is obtained to find the minimum size unobservable malicious data attacks. When the unobservable attack does not exist due to restrictions of meter access, attacks are constructed to minimize the residue energy of attack while guaranteeing a certain level of increase of mean square error. For the control center, a computationally efficient algorithm is derived to detect and localize attacks using the generalized likelihood ratio test regularized by an L_1 norm penalty on the strength of attack.
353 citations
References
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TL;DR: In this article, a complete theory of network observability is presented, starting from a fundamental notion of the observability of a network, a number of basic facts relating to network observations, including unobservable states, observable branches, observable islands, relevancy of measurements, etc.
Abstract: A complete theory of network observability is presented. Starting from a fundamental notion of the observability of a network, a number of basic facts relating to network observability, unobservable states, unobservable branches, observable islands, relevancy of measurements, etc. are derived. Simple and efficient algorithms can be developed based on these basic facts to (i) test network observability, (ii) identify observable islands and (iii) place measurements for observability.
315 citations
01 Jul 1974
TL;DR: A static state estimator is a collection of digital computer programs which convert telemetered data into a reliable estimate of the transmission network structure and state by accounting for small random metering-communication errors and the need for real-time solutions using limited computer time and storage.
Abstract: A static state estimator is a collection of digital computer programs which convert telemetered data into a reliable estimate of the transmission network structure and state by accounting for 1) small random metering-communication errors; 2) uncertainties in system parameter values; 3) bad data due to transients and meter-communication failures; and 4) errors in the network structure due to faulty switch-circuit breaker status information. The overall state estimation process consists of four steps: 1) hypothesize mathematical structure; 2) estimate state vector; 3) detect bad data and/or structure errors; and identify bad data and/or structure errors. The problem is characterized by high dimensionality and the need for real-time solutions using limited computer time and storage. Various methods of solution are discussed and compared.
306 citations
TL;DR: This paper presents fast-decoupled state estimators, using also decoupled detection and identification of bad data, using the sparse inverse matrix method.
Abstract: This paper presents fast-decoupled state estimators, using also decoupled detection and identification of bad data. Bad data is eliminated by pseudo-measurement generation. This procedure avoids gain-matrix retriangulations or the use of modification techniques like Woodbury formula. In the identification process, the diagonal of the covariance matrix of the measurement residuals is calculated using the sparse inverse matrix method. Two main types of fast-decoupled estimators were tested : algorithm- decoupled and model-decoupled. The methods have been tested on IEEE 30-bus load-flow test system, and the FURNAS and CPFL systems that form part of the 835-bus interconnected 15 GW power system of the S.E. Brazil.
225 citations
01 Jan 1979
TL;DR: In this paper, fast decoupled state estimators are used for detection and identification of bad data using pseudo-measurement generation, which avoids gain-matrix retriangulations or the use of modifica- tiontechniques like Woodbury formula.
Abstract: This paperpresentsfast-decoupled stateestimators, using also decoupled detection and identification of baddata.Baddatais eliminated by pseudo-measurement generation. This procedureavoids gain-matrix retriangulations or the use ofmodifica- tiontechniques likeWoodburyformula.Inthe identi- ficationprocess,the diagonalof the covariance matrixof the measurementresidualsis calculated usingthesparseinversematrixmethod.Twomaintypes of fast-decoupled estimators weretested: algorithm- decoupled andmodel-decoupled. Themethodshave been testedon IEEE30-busload-flow testsystem,andthe FURNASandCPFLsystemsthatformpartof the835-bus interconnected 15GWpowersystemof theS.E.Brazil.
218 citations