Detecting and locating cyber and physical stresses in smart grids using the k-nearest neighbour analysis of instantaneous correlation of states

Since modern smart grids have various and deeply coupled cyber-physical components, they are vulnerable to malicious cyber attacks. Although regular defenses including firewall and IDS are deployed, they may be weakened by zero-day vulnerabilities and sophisticated attack schemes. Therefore, defense strategies to mitigate the risk of blackouts during cyber attacks are necessary. This paper proposes a cyber-physical coordinated defense strategy to overcome the disruption and minimize the risk as much as possible. At the cyber layer, a zero-sum multilevel Markovian Stackelberg game is proposed to model sequential actions of the attacker and the defender. The defender distributes defensive resources to protect lines in a real-time manner, according to the attacker's action. If cyber attacks should result in physical outages, defense at the physical layer is then employed. A security-constrained optimal power flow reserving security margin of critical components will be performed to minimize the blackout scale and potential future risk. To solve the corresponding optimization problem and further get the optimal defense strategy, this paper devises a novel “water-pouring” algorithm. Lastly, test results show that the proposed dynamic defense strategy mitigates risk significantly and outperforms existing methods. 

Cyber-Physical Coordinated Risk Mitigation in Smart Grids Based on Attack-Defense Game

Network reconfiguration is a significant strategy to enhance the resiliences of distribution systems against contingencies. This paper proposes a two-stage robust network reconfiguration model to maximize the power supply under the uncertain failures of both lines and renewable generators. In stage 1, before the contingencies are realized, the network topology is reconfigured into several subsystems supplied by conventional generators. Then the contingencies are launched to the reconfigured distribution system, inducing load curtailments. In stage 2, the flexible resources, e.g., conventional and renewable generators, and energy storages, are dispatched to guarantee the maximal power supply under the worst-case contingencies. To solve the two-stage robust model, we develop a column & constraint generation (C&CG) algorithm. The network reconfiguration decision is made in the master problem according to an increasing amount of worst-case scenarios generated by the subproblem. Simulation results demonstrate the performance of the network reconfiguration decision of the two-stage robust model and verify the effectiveness of the C&CG algorithm to deal with the proposed model. 

Two-Stage Robust Distribution Network Reconfiguration Against Failures of Lines and Renewable Generations

Formal modeling and analysis of cyber-physical cross-space attacks in power grid

Network reconfiguration is a significant strategy to enhance the resiliences of distribution systems against contingencies. This paper proposes a two-stage robust network reconfiguration model to maximize the power supply under the uncertain failures of both lines and renewable generators. In stage 1, before the contingencies are realized, the network topology is reconfigured into several subsystems supplied by conventional generators. Then the contingencies are launched to the reconfigured distribution system, inducing load curtailments. In stage 2, the flexible resources, e.g., conventional and renewable generators, and energy storages, are dispatched to guarantee the maximal power supply under the worst-case contingencies. To solve the two-stage robust model, we develop a column & constraint generation (C&CG) algorithm. The network reconfiguration decision is made in the master problem according to an increasing amount of worst-case scenarios generated by the subproblem. Simulation results demonstrate the performance of the network reconfiguration decision of the two-stage robust model and verify the effectiveness of the C&CG algorithm to deal with the proposed model.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09916247.pdf

The false data injection (FDI) attack is a threat to the cyber-physical power system, which may bypass bad data detections and induce substantial damages. To mitigate such a threat, a novel detection method is proposed in this work, which utilizes both the spatial and temporal correlations among measurements to identify anomalies caused by FDI attacks. The vector auto-regression (VAR) is applied to form a set of spatial relation matrices, which reflects correlations between the latest measurement vector and previous several ones. A time sequence of such relation matrix sets is taken as data samples for training an hidden-Markov model (HMM). This HMM recovers the temporal correlations among successive measurement vectors, which helps to distinguish abnormal measurements by estimating their conditional probabilities. Then, a statistical method is introduced to locate the attack source according to numeric variations of the spatial relation matrices. Case studies on an IEEE 9 bus system are presented. Test results validate the efficacy of the proposed FDI attack detection method.

Real-time Detecting False Data Injection Attacks Based on Spatial and Temporal Correlations

In recent years, with the increase in the scale of distribution networks and the type of devices, traditional fault diagnosis approach relying on human experiences is faced with serious challenges. Meanwhile, through testing and online monitoring of various devices, a large amount of operating data has been acquired and stored, providing the necessary foundation for the proposed data analysis. To enhance the efficacy of various distribution devices maintenance, a data-driven and interactive diagnosis platform is designed and implemented. In this platform, physical models of devices, historical diagnosis records and offline experiment data are comprehensively utilized. After extracting association rules between fault characterization and potential defects, a Bayesian network is constructed based on the models of devices and trained by the historical diagnosis records. Using this Bayesian network, the fault diagnosis is made to facilitate function experiments and health examinations of distribution device. An interactive diagnosis method is also proposed, which enables interactive and incremental reasoning based on feedback from test personnel.

Data-Driven and Interactive Fault Diagnosis of Distribution Switches

The embodiment of the invention provides a fault diagnosis method and device for distribution network switch equipment, and the method comprises the steps: building a fault tree model according to thephysical structure and historical diagnosis records of the distribution network switch equipment; performing association rule mining on the historical diagnosis record of the distribution network switch equipment, and adding the mined association rule into the fault tree as a lateral connection to obtain a Bayesian network topology; wherein the association rule is composed of a plurality of defects meeting the minimum support degree and the confidence degree; and training the topology according to the historical diagnosis record to obtain a Bayesian network with determined parameters, solvingthe maximum likelihood equipment defect according to the Bayesian network, and taking the defect with the maximum conditional probability as a fault diagnosis result. According to the embodiment of the invention, the uncertainty of expert competent experience is overcome, the diagnosis result is accurate to equipment parts, and meanwhile, the dependence of manual experience can be reduced to themaximum extent.

A fault diagnosis method and device for distribution network switch equipment

The invention discloses a device fault diagnosis method and a device fault diagnosis system based on a Bayesian network. Wherein the method may be based on a known fault of an input device to be diagnosed, a physical model of a corresponding device and failure condition probability information of the training data set after association rule mining are retrieved from a memory, the optimal fault diagnosis recommendation under the known fault information is given and the optimal fault diagnosis is stored and the optimal fault diagnosis is stored, fully considering the uncertainty of information,the diagnosis results are the optimal solution under the current conditions, not the qualitative value. The database can be updated according to the actual diagnosis results to ensure the accuracy ofdiagnosis.

Equipment fault diagnosis method and system based on bayesian network

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Real-time Detecting False Data Injection Attacks Based on Spatial and Temporal Correlations

Data-Driven and Interactive Fault Diagnosis of Distribution Switches

A fault diagnosis method and device for distribution network switch equipment

Equipment fault diagnosis method and system based on bayesian network