Cyber-physical attacks are the main substantial threats facing the utilization and development of the various smart grid technologies. Among these attacks, false data injection attack represents a main category with its widely varied types and impacts that have been extensively reported recently. In addressing this threat, several detection algorithms have been developed in the last few years. These were either model-based or data-driven algorithms. This paper provides an intensive summary of these algorithms by categorizing them and elaborating on the pros and cons of each category. The paper starts by introducing the various cyber-physical attacks along with the main reported incidents in history. The significance and the impacts of the false data injection attacks are then reported. The concluding remarks present the main criteria that should be considered in developing future detection algorithms for the false data injection attacks.

A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids

Machine learning driven smart electric power systems: Current trends and new perspectives

The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyberattacks more than ever. The complexity and dynamics of cyberattacks require protecting mechanisms to be responsive, adaptive, and scalable. Machine learning, or more specifically deep reinforcement learning (DRL), methods have been proposed widely to address these issues. By incorporating deep learning into traditional RL, DRL is highly capable of solving complex, dynamic, and especially high-dimensional cyber defense problems. This article presents a survey of DRL approaches developed for cyber security. We touch on different vital aspects, including DRL-based security methods for cyber-physical systems, autonomous intrusion detection techniques, and multiagent DRL-based game theory simulations for defense strategies against cyberattacks. Extensive discussions and future research directions on DRL-based cyber security are also given. We expect that this comprehensive review provides the foundations for and facilitates future studies on exploring the potential of emerging DRL to cope with increasingly complex cyber security problems.

pdf/deep-reinforcement-learning-for-cyber-security-1tymm8gk3f.pdf

Deep Reinforcement Learning for Cyber Security.

Networked microgrids (NMGs) provide a promising solution for accommodating various distributed energy resources (DERs) and enhancing the system performance in terms of reliability, resilience, flexibility, and energy efficiency. With the penetration of MGs, the communication-based distributed control is playing an increasingly important role in NMGs for coordinating a multitude of heterogeneous and spatially distributed DERs, which feature enhanced efficiency, reliability, resilience, scalability, and privacy-preserving as compared with conventional centralized control. This article aims to provide a comprehensive survey of distributed control and communication strategies in NMGs. We provide thorough discussions and elaborate on: 1) Essential merits of MGs and NMGs, and their practical implementations; 2) Distributed communication network characteristics and specific operation objectives of NMGs; 3) Classifications of distributed control strategies in NMGs and their salient features; 4) Communication reliability issues concerning data timeliness, data availability, and data accuracy, and the development of countermeasures; 5) Advancements in several promising communication and computation technologies and their potential applications in NMGs.

Distributed Control and Communication Strategies in Networked Microgrids

Reinforcement learning in sustainable energy and electric systems: a survey

False data injection (FDI) attacks intend to threaten the security of power systems. In this paper, a novel strategy of FDI attacks is proposed, which aims to distort normal operation of a power system regulated by automatic voltage controls (AVCs). Such attacks can be launched from a single substation by the attacker who has little knowledge of the whole power grid. The optimal attack strategy is modeled as a partial observable Markov decision process (POMDP). Then, a $\mathcal {Q}$ -learning algorithm with nearest sequence memory is adopted to enable on-line learning and attacking. Stealthy attack strategies are also developed and incorporated into the POMDP model. Various tests are performed upon the IEEE 39-bus systems. Corresponding results verify the efficacy of the proposed attack strategies. The feasibility of independent and data-driven FDI attacks is investigated. Moreover, a bad data detection and correction method are presented based on kernel density estimation to mitigate the disruptive impacts of the proposed FDI attacks. Test results show that this defensive method can help maintain the security of the AVC system, even under heavy system loading.

Evaluation of Reinforcement Learning-Based False Data Injection Attack to Automatic Voltage Control

The false data injection (FDI) attack is a potential threat to the security of smart grids, and therefore, such threats should be assessed carefully. This paper proposes a self-governing FDI attack method with exploitation and exploration mechanisms and then evaluates its threat to power systems. The attack is executed by viruses embedded in monitoring systems in substations. First, the FDI attack is formulated as a partially observable Markov decision process. Then, an improved online reinforcement learning method called nearest sequence memory Q-learning is adopted to make the attack more effective. Finally, propagation, an inherent property of viruses, is described using a propagation-evolution model that serves as the exploration mechanism for the proposed FDI attack. To validate the proposed attack method, cosimulations of daily operations of the IEEE 39-bus system are performed in which both the automatic voltage control system and the proposed FDI attack are modeled. Test results show that the proposed FDI method can cause voltage collapse even if only a few substations are infected.

Power System Security Under False Data Injection Attacks With Exploitation and Exploration Based on Reinforcement Learning

The applications of super-resolution (SR) technology in the field of image completion are successful. Nevertheless, industry applications demand not only image completion but also the topology and time-series completion. In this article, the SR technology on a topology graph is studied in the scenario of recovering measurements in power distribution systems for cost saving and security & stability improvement. The power flow and voltage magnitude measurements on feeders are reported at different frequencies. In this article, a new data completion method considering distribution system topology is proposed. Firstly, the graph convolutional neural network (GCN) is used for spatial-temporal convolution on a graph, and then the power system state estimation (SE) is used introducing the physical constraints. This method realizes the super-resolution of distribution system measurements, improves the state awareness of distribution systems. Hence, it helps to improve the efficiency of distribution network operation and to reduce equipment failures.

/pdf/temporal-graph-super-resolution-on-power-distribution-1ewg2ttdvq.pdf

Temporal Graph Super Resolution on Power Distribution Network Measurements

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

False data injection (FDI) attacks are serious threats to a cyber‐physical power system (CPPS), which may be launched by a malicious software or virus accessing only the measurements from one substation. This study proposes a novel attack method named the temporal FDI (TFDI) attack. Namely, the virus makes decisions based on temporal observations of the CPPS, and the attack is driven by a deep Q network (DQN) algorithm. As DQN takes vectors of continuous variables as input states, the proposed method is free of the state space explosion problem, which helps the virus to learn the optimal attack strategy efficiently. Moreover, for adopting time‐series measurements as quasi‐dynamic observations, long short‐term memory cells are employed as a layer of the Q network. The TFDI attack enables the virus to discern trends of load variations and enhance the attack’s effectiveness. Meanwhile, a countermeasure is also presented to detect the proposed FDI attack. Binary classifiers are trained for each bus to detect suspicious local measurements according to their deviations from system‐state manifolds. When suspicious measurements are spotted frequently, the corresponding bus is believed to be under FDI attacks. Test cases validate the efficacy of the proposed FDI attack method as well as its countermeasure.

Temporal false data injection attack and detection on cyber‐physical power system based on deep reinforcement learning

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