Biblio

The cyber-physical security of smart grid is of great importance since it directly concerns the normal operating of a system. Recently, researchers found that organized sequential attacks can incur large-scale cascading failure to the smart grid. In this paper, we focus on the line-switching sequential attack, where the attacker aims to trip transmission lines in a designed order to cause significant system failures. Our objective is to identify the critical line-switching attack sequence, which can be instructional for the protection of smart grid. For this purpose, we develop an evolutionary computation based vulnerability analysis framework, which employs particle swarm optimization to search the critical attack sequence. Simulation studies on two benchmark systems, i.e., IEEE 24 bus reliability test system and Washington 30 bus dynamic test system, are implemented to evaluate the performance of our proposed method. Simulation results show that our method can yield a better performance comparing with the reinforcement learning based approach proposed in other prior work.

Traditional power grid security schemes are being replaced by highly advanced and efficient smart security schemes due to the advancement in grid structure and inclusion of cyber control and monitoring tools. Smart attackers create physical, cyber, or cyber-physical attacks to gain the access of the power system and manipulate/override system status, measurements and commands. In this paper, we formulate the environment for the attacker-defender interaction in the smart power grid. We provide a strategic analysis of the attacker-defender strategic interaction using a game theoretic approach. We apply repeated game to formulate the problem, implement it in the power system, and investigate for optimal strategic behavior in terms of mixed strategies of the players. In order to define the utility or cost function for the game payoffs calculation, generation power is used. Attack-defense budget is also incorporated with the attacker-defender repeated game to reflect a more realistic scenario. The proposed game model is validated using IEEE 39 bus benchmark system. A comparison between the proposed game model and the all monitoring model is provided to validate the observations.

This work proposes a moving target defense (MTD) strategy to detect coordinated cyber-physical attacks (CCPAs) against power grids. A CCPA consists of a physical attack, such as disconnecting a transmission line, followed by a coordinated cyber attack that injects false data into the sensor measurements to mask the effects of the physical attack. Such attacks can lead to undetectable line outages and cause significant damage to the grid. The main idea of the proposed approach is to invalidate the knowledge that the attackers use to mask the effects of the physical attack by actively perturbing the grid's transmission line reactances using distributed flexible AC transmission system (D-FACTS) devices. We identify the MTD design criteria in this context to thwart CCPAs. The proposed MTD design consists of two parts. First, we identify the subset of links for D-FACTS device deployment that enables the defender to detect CCPAs against any link in the system. Then, in order to minimize the defense cost during the system's operational time, we use a game-theoretic approach to identify the best subset of links (within the D-FACTS deployment set) to perturb which will provide adequate protection. Extensive simulations performed using the MATPOWER simulator on IEEE bus systems verify the effectiveness of our approach in detecting CCPAs and reducing the operator's defense cost.

A process of critical transmission lines identification in presented here. The criticality is based on network flow, which is essential for power grid connectivity monitoring as well as vulnerability assessment. The proposed method can be utilized as a supplement of traditional situational awareness tool in the energy management system of the power grid control center. At first, a flow network is obtained from topological as well as functional features of the power grid. Then from the duality property of a linear programming problem, the maximum flow problem is converted to a minimum cut problem. Critical transmission lines are identified as a solution of the dual problem. An overall set of transmission lines are identified from the solution of the network flow problem. Simulation of standard IEEE test cases validates the application of the method in finding critical transmission lines of the power grid.

The risk of large-scale blackouts and cascading failures in power grids can be due to vulnerable transmission lines and lack of proper remediation techniques after recognizing the first failure. In this paper, we assess the vulnerability of a system using fault chain theory and a power flow-based method, and calculate the probability of large-scale blackout. Further, we consider a Remedial Action Scheme (RAS) to reduce the vulnerability of the system and to harden the critical components against intentional attacks. To identify the most critical lines more efficiently, a new vulnerability index is presented. The effectiveness of the new index and the impact of the applied RAS is illustrated on the IEEE 14-bus test system.

The identification of transmission sections is used to improve the efficiency of monitoring the operation of the power grid. In order to test the validity of transmission sections identified, an assessment process is necessary. In addition, Transmission betweenness, an index for finding the key transmission lines in the power grid, should also be verified. In this paper, chain attack is assumed to check the weak links in the grid, thus verifying the transmission betweenness implemented for the system. Moreover, the line outage distribution factors (LODFs) are used to quantify the change of power flow when the leading line in transmission sections breaks down, so that the validity of transmission sections can be proved. Case studies based on IEEE 39 and IEEE 118 -bus system proved the effectiveness of the proposed method.

Reliable operation of power systems is a primary challenge for the system operators. With the advancement in technology and grid automation, power systems are becoming more vulnerable to cyber-attacks. The main goal of adversaries is to take advantage of these vulnerabilities and destabilize the system. This paper describes a game-theoretic approach to attacker / defender modeling in power systems. In our models, the attacker can strategically identify the subset of substations that maximize damage when compromised. However, the defender can identify the critical subset of substations to protect in order to minimize the damage when an attacker launches a cyber-attack. The algorithms for these models are applied to the standard IEEE-14, 39, and 57 bus examples to identify the critical set of substations given an attacker and a defender budget.

An unobservable false data injection (FDI) attack on AC state estimation (SE) is introduced and its consequences on the physical system are studied. With a focus on understanding the physical consequences of FDI attacks, a bi-level optimization problem is introduced whose objective is to maximize the physical line flows subsequent to an FDI attack on DC SE. The maximization is subject to constraints on both attacker resources (size of attack) and attack detection (limiting load shifts) as well as those required by DC optimal power flow (OPF) following SE. The resulting attacks are tested on a more realistic non-linear system model using AC state estimation and ACOPF, and it is shown that, with an appropriately chosen sub-network, the attacker can overload transmission lines with moderate shifts of load.

Usually, the air gap will appear inside the composite insulators and it will lead to serious accident. In order to detect these internal defects in composite insulators operated in the transmission lines, a new non-destructive technique has been proposed. In the study, the mathematical analysis model of the composite insulators inner defects, which is about heat diffusion, has been build. The model helps to analyze the propagation process of heat loss and judge the structure and defects under the surface. Compared with traditional detection methods and other non-destructive techniques, the technique mentioned above has many advantages. In the study, air defects of composite insulators have been made artificially. Firstly, the artificially fabricated samples are tested by flash thermography, and this method shows a good performance to figure out the structure or defects under the surface. Compared the effect of different excitation between flash and hair drier, the artificially samples have a better performance after heating by flash. So the flash excitation is better. After testing by different pollution on the surface, it can be concluded that different pollution don't have much influence on figuring out the structure or defect under the surface, only have some influence on heat diffusion. Then the defective composite insulators from work site are detected and the image of defect is clear. This new active thermography system can be detected quickly, efficiently and accurately, ignoring the influence of different pollution and other environmental restrictions. So it will have a broad prospect of figuring out the defeats and structure in composite insulators even other styles of insulators.

As a consequence of the recent development of situational awareness technologies for smart grids, the gathering and analysis of data from multiple sources offer a significant opportunity for enhanced fault diagnosis. In order to achieve improved accuracy for both fault detection and classification, a novel combined data analytics technique is presented and demonstrated in this paper. The proposed technique is based on a segmented approach to Bayesian modelling that provides probabilistic graphical representations of both electrical power and data communication networks. In this manner, the reliability of both the data communication and electrical power networks are considered in order to improve overall power system transmission line fault diagnosis.

Transmission lines' monitoring systems produce a large amount of data that hinders faults diagnosis. For this reason, approaches that can acquire and automatically interpret the information coming from lines' monitoring are needed. Furthermore, human errors stemming from operator dependent real-time decision need to be reduced. In this paper a multiple faults diagnosis method to determine transmission lines' operating conditions is proposed. Different scenarios, including insulator chains contamination with different types and concentrations of pollutants were modeled by equivalents circuits. Their performance were characterized by leakage current (LC) measurements and related to specific fault modes. Features extraction's algorithm relying on the difference between normal and faulty conditions were used to define qualitative trends for the diagnosis of various fault modes.

As societies are becoming more dependent on the power grids, the security issues and blackout threats are more emphasized. This paper proposes a new graph model for online visualization and assessment of power grid security. The proposed model integrates topology and power flow information to estimate and visualize interdependencies between the lines in the form of line dependency graph (LDG) and immediate threats graph (ITG). These models enable the system operator to predict the impact of line outage and identify the most vulnerable and critical links in the power system. Line Vulnerability Index (LVI) and Line Criticality Index (LCI) are introduced as two indices extracted from LDG to aid the operator in decision making and contingency selection. This package can be useful in enhancing situational awareness in power grid operation by visualization and estimation of system threats. The proposed approach is tested for security analysis of IEEE 30-bus and IEEE 118-bus systems and the results are discussed.

Power networks can be modeled as networked structures with nodes representing the bus bars (connected to generator, loads and transformers) and links representing the transmission lines. In this manuscript we study cascaded failures in power networks. As network structures we consider IEEE 118 bus network and a random spatial model network with similar properties to IEEE 118 bus network. A maximum flow based model is used to find the central edges. We study cascaded failures triggered by both random and targeted attacks to the edges. In the targeted attack the edge with the maximum centrality value is disconnected from the network. A number of metrics including the size of the largest connected component, the number of failed edges, the average maximum flow and the global efficiency are studied as a function of capacity parameter (edge critical load is proportional to its capacity parameter and nominal centrality value). For each case we identify the critical capacity parameter by which the network shows resilient behavior against failures. The experiments show that one should further protect the network for a targeted attack as compared to a random failure.

Cascading failure is an intrinsic threat of power grid to cause enormous cost of society, and it is very challenging to be analyzed. The risk of cascading failure depends both on its probability and the severity of consequence. It is impossible to analyze all of the intrinsic attacks, only the critical and high probability initial events should be found to estimate the risk of cascading failure efficiently. To recognize the critical and high probability events, a cascading failure analysis model for power transmission grid is established based on complex network theory (CNT) in this paper. The risk coefficient of transmission line considering the betweenness, load rate and changeable outage probability is proposed to determine the initial events of power grid. The development tendency of cascading failure is determined by the network topology, the power flow and boundary conditions. The indicators of expected percentage of load loss and line cut are used to estimate the risk of cascading failure caused by the given initial malfunction of power grid. Simulation results from the IEEE RTS-79 test system show that the risk of cascading failure has close relations with the risk coefficient of transmission lines. The value of risk coefficient could be useful to make vulnerability assessment and to design specific action to reduce the topological weakness and the risk of cascading failure of power grid.

Smart grids utilize computation and communication to improve the efficacy and dependability of power generation, transmission, and distribution. As such, they are among the most critical and complex cyber-physical systems. The success of smart grids in achieving their stated goals is yet to be rigorously proven. In this paper, our focus is on improvements (or lack thereof) in reliability. We discuss vulnerabilities in the smart grid and their potential impact on its reliability, both generally and for the specific example of the IEEE-14 bus system. We conclude the paper by presenting a preliminary Markov imbedded systems model for reliability of smart grids and describe how it can be evolved to capture the vulnerabilities discussed.
 

The modern society increasingly relies on electrical service, which also brings risks of catastrophic consequences, e.g., large-scale blackouts. In the current literature, researchers reveal the vulnerability of power grids under the assumption that substations/transmission lines are removed or attacked synchronously. In reality, however, it is highly possible that such removals can be conducted sequentially. Motivated by this idea, we discover a new attack scenario, called the sequential attack, which assumes that substations/transmission lines can be removed sequentially, not synchronously. In particular, we find that the sequential attack can discover many combinations of substation whose failures can cause large blackout size. Previously, these combinations are ignored by the synchronous attack. In addition, we propose a new metric, called the sequential attack graph (SAG), and a practical attack strategy based on SAG. In simulations, we adopt three test benchmarks and five comparison schemes. Referring to simulation results and complexity analysis, we find that the proposed scheme has strong performance and low complexity.