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.

False data injection attacks against state estimation in electric power grids

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 the interacting bad measurements introduced by arbitrary, non-random causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.In this paper, we present a new class of attacks, called false data injection attacks, against state estimation in electric power grids. We show that an attacker can exploit the configuration of a power system to launch such attacks to successfully introduce arbitrary errors into certain state variables while bypassing existing techniques for bad measurement detection. Moreover, we look at two realistic attack scenarios, in which the attacker is either constrained to some specific meters (due to the physical protection of the 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, which can not only change the results of state estimation, but also modify the results in arbitrary ways. We demonstrate the success of these attacks through simulation using IEEE test systems. Our results indicate that security protection of the electric power grid must be revisited when there are potentially malicious attacks.

The development of a trustworthy smart grid requires a deeper understanding of potential impacts resulting from successful cyber attacks. Estimating feasible attack impact requires an evaluation of the grid's dependency on its cyber infrastructure and its ability to tolerate potential failures. A further exploration of the cyber-physical relationships within the smart grid and a specific review of possible attack vectors is necessary to determine the adequacy of cybersecurity efforts. This paper highlights the significance of cyber infrastructure security in conjunction with power application security to prevent, mitigate, and tolerate cyber attacks. A layered approach is introduced to evaluating risk based on the security of both the physical power applications and the supporting cyber infrastructure. A classification is presented to highlight dependencies between the cyber-physical controls required to support the smart grid and the communication and computations that must be protected from cyber attack. The paper then presents current research efforts aimed at enhancing the smart grid's application and infrastructure security. Finally, current challenges are identified to facilitate future research efforts.

Cyber–Physical System Security for the Electric Power Grid

This paper discusses the state of the art in electric power system state estimation. Within energy management systems, state estimation is a key function for building a network real-time model. A real-time model is a quasi-static mathematical representation of the current conditions in an interconnected power network. This model is extracted at intervals from snapshots of real-time measurements (both analog and status). The new modeling needs associated with the introduction of new control devices and the changes induced by emerging energy markets are making state estimation and its related functions more important than ever.

Electric power system state estimation

Malicious attacks against power systems are investigated, in which an adversary controls a set of meters and is able to alter the measurements from those meters. Two regimes of attacks are considered. The strong attack regime is where the adversary attacks a sufficient number of meters so that the network state becomes unobservable by the control center. For attacks in this regime, the smallest set of attacked meters capable of causing network unobservability is characterized using a graph theoretic approach. By casting the problem as one of minimizing a supermodular graph functional, the problem of identifying the smallest set of vulnerable meters is shown to have polynomial complexity. For the weak attack regime where the adversary controls only a small number of meters, the problem is examined from a decision theoretic perspective for both the control center and the adversary. For the control center, a generalized likelihood ratio detector is proposed that incorporates historical data. For the adversary, the trade-off between maximizing estimation error at the control center and minimizing detection probability of the launched attack is examined. An optimal attack based on minimum energy leakage is proposed.

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Malicious Data Attacks on the Smart Grid

The extended equal area criterion (EEAC) for online transient stability analysis is considered with the following objectives. The first is to state systematically its main hypotheses and key conditions, justify the former, and suggest means to guarantee the latter. The identification and error analysis of critical machines are among the investigated issues. The second is to scan all possible types of instabilities likely to arise in practice and devise means to treat them. The extension of the EEAC to cases beyond the so-called first-swing stability makes it more robust than all direct methods developed up to now. The third objective is to extract essential information out of a large body of simulations and show that the above improvements and extensions enhance the EEAC accuracy and its capability to work properly even under stringent conditions. Possible EEAC applications are also discussed, and uses of the method as such or as an auxiliary technique for more sophisticated approaches are suggested. >

Extended equal area criterion justifications, generalizations, applications

The anomalous data identification procedures existing today in power system state estimation become problematic-if not totally unefficient-under stringent conditions, such as multiple and interacting bad data. The identification method presented in this paper attempts to alleviate these difficulties. It consists in :(i) computing measurement error estimates and using them as the random variables of concern;(ii) making decisions on the basis of a hypothesis testing which takes into account their statistical properties. Two identification techniques are then derived and further investigated and assessed by means of a realistic illustrative example. Conceptually novel, the identification methodology is thus shown to lead to practical procedures which are efficient, reliable and workable under all theoretically feasible conditions.

Hypothesis Testing Identification: A New Method For Bad Data Analysis In Power System State Estimation

A hierarchical concept is used to solve the static state estimation problem for large-scale composite power systems. The solution is obtained by performing a two-level calculation. In the lower level, a conventional state estimation is carried out simultaneously for all subsystems. The coordination of these local estimations is realized in the upper level. One of the main contributions of the paper lies in the construction of an appropriate second-level algorithm. Its suitability and also the main features of the overall procedure are then explored and illustrated on the basis of the Belgian 380-220-150 kV transmission network. Comparisons with the standard "integrated" state estimation are also performed.

A Two-Level Static State Estimator for Electric Power Systems

The identification techniques available today are first classified into three broad classes. Their behaviour with respect to selected criteria are then explored and assessed. Further, a series of simulations are carried out with various types of bad data. Investigating the way these identification techniques behave allows completing and validating the theoretical comparisons and conclusions.

Bad Data Identification Methods In Power System State Estimation-A Comparative Study

This paper intends to give a unified survey of methods appropriate for solving the state estimation problem in large-scale electric power systems After a first overview of the various approaches proposed up to now, the most suitable among them are described, examined and compared The comparisons are carried out on the basis of selected criteria evolving estimation properties of the resulting algorithms, along with their organization possibilities and their capabilities of handling some important satellite functions

Th. Van Cutsem

Papers

Extended equal area criterion justifications, generalizations, applications

Hypothesis Testing Identification: A New Method For Bad Data Analysis In Power System State Estimation

A Two-Level Static State Estimator for Electric Power Systems

Bad Data Identification Methods In Power System State Estimation-A Comparative Study

Critical Survey of Hierarchical Methods for State Estimation of Electric Power Systems