Cyber-physical Power System Research Articles

Operational planning steps in smart electric power delivery system

This paper presents a comprehensive review of advanced technologies with various control approaches in terms of their respective merits and outcomes for power grids. Distributed energy storage control is classified into automatic voltage regulator and load frequency control according to corresponding functionalities. These control strategies maintain a power balance between generation and demand. Besides, three basic electric vehicle charging technologies can be distinguished, i.e. stationary, quasi-dynamic and dynamic control. For realizing charge-sustaining operation at minimum cost quasi-dynamic and dynamic strategies are adopted for in-route charging, while stationary control can only be utilized when the electric vehicle is in stationary mode. Moreover, power system frequency stability and stabilization techniques in non-synchronous generator systems are reviewed in the paper. Specifically, a synchronverter can damp power system oscillations and ensure stability by providing virtual inertia. Furthermore, it is crucial to manage the massive information and ensure its security in the smart grid. Therefore, several attack detection and mitigation schemes against cyber-attacks are further presented to achieve reliable, resilient, and stable operation of the cyber-physical power system. Thus, bidirectional electrical power flows with two-way digital control and communication capabilities have poised the energy producers and utilities to restructure the conventional power system into a robust smart distribution grid. These new functionalities and applications provide a pathway for clean energy technology. Finally, future research trends on smart grids such as IoT-based communication infrastructure, distributed demand-response with artificial intelligence and machine learning solutions, and synchrophasor-based wide-area monitoring protection and control (WAMPC) are examined in the present study.

Reliability Evaluation of Cyber-Physical Power Systems Considering Random Failures in Measurement and Remote Control

In integrated cyber-physical power systems (CPPS), the malfunctions of cyber system deeply affect the dispatcher’s remedial action scheme, and thus have a significant impact on the reliability of the physical power system. The random failures of electrical devices in physical power system and the random outages of measurement and remote control in cyber system are both considered for the CPPS reliability evaluation in this paper. Firstly, the component outage models in physical, access, backbone and application layers are presented. Then, a linearized DC state estimation (SE) model is introduced and the SE errors incurred by measurement failures, i.e. bad and lost measurements, are interpreted. Moreover, the impact of SE errors and remote control failures on the corrective action scheme of the control center during the fault recovery process is simulated by a novel modification of the classical optimal load shedding (OLS) model. Finally, to verify the effectiveness and validity of the proposed CPPS reliability evaluation method, the communication network topologies of the cyber system for the RBTS and IEEE-RTS96 are designed, and then the reliability evaluation based on the presented component outage models and the novel modified OLS model are conducted by the non-sequential Monte Carlo simulation (NMCS).

Impact of wind power uncertainty on cascading failure in cyber–physical power systems

The increasing penetration of wind power may have a profound effect on the cascading dynamics of power delivery systems. In this study, we consider the impacts of packet traffic congestion, power overloading and network interaction on the failure evolution, and investigate the impacts of wind uncertainty and penetration level on the vulnerability of the cyber–physical power system to cascading failure. The proposed model takes into account the modularity of the coupled system, and incorporates both uncertainty analysis and stochastic approach into the line trip mechanism. The discrete packet traffic model is adopted to describe the influence of communication delay on the cascading failure of the power grid. Moreover, we investigate the effect of community structure on the robustness of the modular coupled system. Results indicate that increased wind uncertainty can lead to the occurrence of cascading events and higher wind power penetration increases the vulnerability of the coupled system to cascading failure. In addition, the robustness of the modular coupled system can be enhanced by dissolving the community structure of the communication network, and the timely and efficient data transmission in the communication network can make the cyber-coupled power system more robust.

Generative Adversarial Network-Based Scheme for Diagnosing Faults in Cyber-Physical Power Systems.

This paper presents a novel diagnostic framework for distributed power systems that is based on using generative adversarial networks for generating artificial knockoffs in the power grid. The proposed framework makes use of the raw data measurements including voltage, frequency, and phase-angle that are collected from each bus in the cyber-physical power systems. The collected measurements are firstly fed into a feature selection module, where multiple state-of-the-art techniques have been used to extract the most informative features from the initial set of available features. The selected features are inputs to a knockoff generation module, where the generative adversarial networks are employed to generate the corresponding knockoffs of the selected features. The generated knockoffs are then fed into a classification module, in which two different classification models are used for the sake of fault diagnosis. Multiple experiments have been designed to investigate the effect of noise, fault resistance value, and sampling rate on the performance of the proposed framework. The effectiveness of the proposed framework is validated through a comprehensive study on the IEEE 118-bus system.

The impact of synchronous distributed control period on inverter-based cyber–physical microgrids stability with time delay

Inverter-based microgrids under fully synchronous distributed control have broad prospects in multi-agent-based energy systems. Such a system is a typical cyber–physical system. Studying the impact of a synchronous control period on system stability is the pre-requisite for practical deployment of a distributed control architecture with a proper synchronous period. However, simulation-based methods provide little theoretical insight into how synchronous control period influences system stability. Additionally, control-period determination methods based on the convergence analysis of a distributed algorithm cannot sufficiently consider the dynamic characteristic of a physical inverter-based microgrid. In this paper, a synchronized cyber–physical state-transition model of distributed inverter-based microgrids considering multiple time delays from a cyber–physical perspective is proposed. The stability criterion of the system under certain synchronous control periods with multiple time delays is obtained. Furthermore, considering multiple stochastic switching delays with infinite combinations of states, the corresponding stability criterion is also obtained based on switched system theory. The stable interval of the synchronous control period for such cyber–physical power systems can be determined based on the proposed stability criteria and algorithm. A real-world microgrid under fully distributed frequency control is tested and simulated by Simulink simulations to verify the accuracy of the obtained stable synchronous control period.

Adaptive observer-based attack location and defense strategy in smart grid

This article mainly focuses on the attack location and defense of cyber-physical power systems under dynamic load altering attacks. Taking the smart grid as an example, first, the cyber-physical power system model under attack is established to provide conditions for attack detection. On this basis, an adaptive observer with [Formula: see text] performance is designed to realize detection and location through residual signal and attack estimation, respectively. Furthermore, a power redistribution strategy based on optimal economic scheduling is presented to realize attack defense by means of the observer data. Finally, theoretical analysis proves the feasibility of the algorithm, and simulation cases prove the effectiveness of attack location and defense strategies.

A Bi-Level Model for Detecting and Correcting Parameter Cyber-Attacks in Power System State Estimation

Power system state estimation is an important component of the status and healthiness of the underlying electric power grid real-time monitoring. However, such a component is prone to cyber-physical attacks. The majority of research in cyber-physical power systems security focuses on detecting measurements False-Data Injection attacks. While this is important, measurement model parameters are also a most important part of the state estimation process. Measurement model parameters though, also known as static-data, are not monitored in real-life applications. Measurement model solutions ultimately provide estimated states. A state-of-the-art model presents a two-step process towards simultaneous false-data injection security: detection and correction. Detection steps are χ2 statistical hypothesis test based, while correction steps consider the augmented state vector approach. In addition, the correction step uses an iterative solution of a relaxed non-linear model with no guarantee of optimal solution. This paper presents a linear programming method to detect and correct cyber-attacks in the measurement model parameters. The presented bi-level model integrates the detection and correction steps. Temporal and spatio characteristics of the power grid are used to provide an online detection and correction tool for attacks pertaining the parameters of the measurement model. The presented model is implemented on the IEEE 118 bus system. Comparative test results with the state-of-the-art model highlight improved accuracy. An easy-to-implement model, built on the classical weighted least squares solution, without hard-to-derive parameters, highlights potential aspects towards real-life applications.

Modeling of Time-Delayed Distributed Cyber-Physical Power Systems for Small-Signal Stability Analysis

Compared with a power system under centralized control, a power system under fully distributed control has stronger interdependence between its physical side and cyber side. Such a system is a typical cyber-physical system (CPS). The time delay problem is one of the most critical issues in the application of distributed control for power systems. First, a distributed information flow model is established considering multiple sources of time delay, especially the communication delay between neighboring agents. Second, combining the dynamics of physical power systems, a general model of time-delayed CPS under fully distributed control is proposed, especially for inverter-based microgrids. Third, the stability of the time-delayed CPS is analyzed based on the proposed model with a critical eigenvalue tracking algorithm. A real-world microgrid with DGs under fully distributed frequency control is tested for small-signal stability analysis. The findings show that the stability of the distributed CPS with multiple sources of time delay depends on not only the absolute value of time delays but also the coordination of time delays and that the delay merging property in centralized mode is not relevant in a distributed CPS. The accuracy of the model and the obtained stability margin are verified by Simulink simulations.

Man‐in‐the‐middle attacks and defence in a power system cyber‐physical testbed

Man-in-The-Middle (MiTM) attacks present numerous threats to a smart grid. In a MiTM attack, an intruder embeds itself within a conversation between two devices to either eavesdrop or impersonate one of the devices, making it appear to be a normal exchange of information. Thus, the intruder can perform false data injection (FDI) and false command injection (FCI) attacks that can compromise power system operations, such as state estimation, economic dispatch, and automatic generation control (AGC). Very few researchers have focused on MiTM methods that are difficult to detect within a smart grid. To address this, we are designing and implementing multi-stage MiTM intrusions in an emulation-based cyber-physical power system testbed against a large-scale synthetic grid model to demonstrate how such attacks can cause physical contingencies such as misguided operation and false measurements. MiTM intrusions create FCI, FDI, and replay attacks in this synthetic power grid. This work enables stakeholders to defend against these stealthy attacks, and we present detection mechanisms that are developed using multiple alerts from intrusion detection systems and network monitoring tools. Our contribution will enable other smart grid security researchers and industry to develop further detection mechanisms for inconspicuous MiTM attacks.

Smarter Grid in the 5G Era: A Framework Integrating Power Internet of Things With a Cyber Physical System

As the energy infrastructure of smart cities, smart grid upgrades traditional power grid systems with state-of-the-art information and communication technologies. In particular, as the full deployment of the Internet of Things in the power grid (a.k.a. power Internet of Things or PIoT), the newly introduced information flow together with inherent energy flow makes it more efficient for power generation, transmission, distribution, and consumption. To further exploit the precious energy and the latest 5G technologies, this article boosts to add a value flow in the smart grid, mainly including the value created by innovative services and market mechanisms and the value added by the information flow. Specifically, by integrating PIoT with cyber-physical systems, this article sketches a conceptual framework of the cyber-physical power system (CPPS). The CPPS carries out holistic perception and ubiquitous connection of distributed energy sources and electrical facilities and builds up a smarter power grid with global information interaction, intelligent decision-making, and real-time agile control. Finally, for illustration purposes, we conduct a case study regarding an intelligent home management system.

Intrusion Detection on Apache Spark Platform in Big data and Machine Learning Techniques

With the rising cyber-physical power systems and emerging danger of cyber-attacks, the traditional power services are faced with higher risks of being compromised, as vulnerabilities in cyber communications can be broken to cause material damage. Therefore, adjustment needs to be made in the present control scheme plan methods to moderate the impact of possible attacks on service quality. This paper, focuses on the service of synchronized source-load contribution in main frequency regulation, a weakness study is performed with model the attack intrusion process, and the risk review of the service is made by further model the attack impacts on the service’s bodily things. On that basis, the customary synchronized reserve allotment optimization model is adapted and the allocation scheme is correct according to the cyber-attack impact. The proposed alteration methods are validating through a case study, showing efficiency in defensive alongside the cyber-attack impacts.

Design and evaluation of a cyber‐physical testbed for improving attack resilience of power systems

A power system is a complex cyber-physical system whose security is critical to its function. A major challenge is to model and analyze its communication pathways with respect to cyber threats. To achieve this, the design and evaluation of a cyber-physical power system (CPPS) testbed called Resilient Energy Systems Lab (RESLab) is presented that captures realistic cyber, physical, and protection system features. RESLab is architected to be a fundamental tool for studying and improving the resilience of complex CPPS to cyber threats. The cyber network is emulated using Common Open Research Emulator (CORE) that acts as a gateway for the physical and protection devices to communicate. The physical grid is simulated in the dynamic time frame using PowerWorld Dynamic Studio (PWDS). The protection components are modeled with both PWDS and physical devices including the SEL Real-Time Automation Controller (RTAC). Distributed Network Protocol 3 (DNP3) is used to monitor and control the grid. Then, exemplifying the design and validation of these tools, this paper presents four case studies on cyber-attack and defense using RESLab, where we demonstrate false data and command injection using Man-in-the-Middle and Denial of Service attacks and validate them on a large-scale synthetic electric grid.

A cross-space cascading failure hazard assessment method considering betweenness centrality and power loss

In order to accurately assess the hazard caused by cross-space cascading failure in the cyber-physical power system, we propose a quantitative assessment method. This method builds a comprehensive framework of assessment that takes into account the betweenness centrality of attack graph and the consequences of failure. The betweenness centrality of each node in the attack graph is used to characterize the frequency of failure. By calculating the number of all nodes on each attack path, the frequency of a certain fault is calculated. The power loss of physical node caused by each cross-space cascading failure is used to characterize the consequences of the fault, which is combined with the frequency of failure and the urgency of troubleshooting to calculate the hazard assessment value of the cross-space cascading failure. Finally, a variety of cross-space cascading failures are implemented in the simulation environment of local cyber-physical power system and their hazards are evaluated. The effectiveness of the proposed assessment method is verified.

A Gene Importance based Evolutionary Algorithm (GIEA) for identifying critical nodes in Cyber–Physical Power Systems

Protecting the critical nodes of a Cyber–Physical Power System (CPPS) is an effective strategy for mitigating the risk of incurring large-scale blackouts. A Gene Importance based Evolutionary Algorithm (GIEA) is proposed to identify a set of critical k nodes by maximizing the total load loss received by end-users. GIEA adopts an importance-based evolutionary strategy to improve the algorithm’s convergence and accuracy, in which the initial node importance metrics are assessed based on dynamic power flows and topology information. Both performance contribution (PC) and coupling failure impact (CFI) are considered in our importance evaluation framework. The impacts of different types of communication nodes on power networks are integrated into the proposed cascading failure model and CFI assessment. Based on the coupling and interdependence information, the strong coupling node pairs are identified to reduce the dimension of the decision vector to improve the computational efficiency of GIEA. The effectiveness and superiority of the proposed methods are illustrated through an example of a coupling CPPS consisting of the IEEE 30-bus model and a communication network with the small-world structure.

Review of Modeling and Simulation Methods for Cyber Physical Power System

With the development of informatization and intellectualization technology, the power system has become the cyber-physical power system (CPPS) with deep integration of the cyber side and the physical side. As the support system of smart grids with new forms and features, the CPPS considers the coupling relationship between the communication network and the physical power grid, and is a research hotspot in the field of smart grids. This paper provides an overview of the CPPS framework and describes the interaction between the communication network and the physical power grid. Then the mainstream modeling and simulation methods of CPPS are summarized, and the advantages and disadvantages of each method are pointed out. In the end, the paper looks forward to the possible research directions of CPPS in the future. The article provides researchers with different perspectives on the dynamic model and simulation methods of CPPS.

Coordinated Cyber-Attack Detection Model of Cyber-Physical Power System Based on the Operating State Data Link

Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets. This paper proposes a model for cyber and physical data fusion using a data link for detecting attacks on a Cyber–Physical Power System (CPPS). The two-step principal component analysis (PCA) is used for classifying the system’s operating status. An adaptive synthetic sampling algorithm is used to reduce the imbalance in the categories’ samples. The loss function is improved according to the feature intensity difference of the attack event, and an integrated classifier is established using a classification algorithm based on the cost-sensitive gradient boosting decision tree (CS-GBDT). The simulation results show that the proposed method provides higher accuracy, recall, and F-Score than comparable algorithms.

Cyber‐physical component ranking for risk sensitivity analysis using betweenness centrality

This article proposes a model for critical component ranking in power system risk analysis using a proposed cyber-physical betweenness centrality (CPBC) index. Risk assessment, as part of the contingency analysis, is a critical activity that can identify and evaluate component outages that lead to system vulnerability, aiding operators to improve resilience. A power system cyber-physical risk assessment model is proposed that calculates and offers an efficient protection strategy to the system operator based on component vulnerability to adversaries and the impact of compromised assets on the system operation. We present the CPBC index, which traverses generated attack graphs to rank components according to their importance in reducing adversary impact on the power system. The CPBC extends upon betweenness centrality and integrates into analysis, the services and security cost of communications between system components, as well as the likelihood of component exploitation as an adversary medium to the target relays. The proposed model recommends actions, taking into account the interconnections between cyber and physical components as well as cyber-induced Common Vulnerabilities and Exposure scores associated with these connections, thus protecting critical components. The proposed model is implemented on the Cyber-Physical Situational Awareness 8-substation and extended IEEE 300-bus cyber-physical power system models, and results are presented on the impacts of the proposed component ranking model on the security-aware operation of the power system.

Neural Network-Based Intelligent Computing Algorithms for Discrete-Time Optimal Control with the Application to a Cyberphysical Power System

Adaptive dynamic programming (ADP), which belongs to the field of computational intelligence, is a powerful tool to address optimal control problems. To overcome the bottleneck of solving Hamilton–Jacobi–Bellman equations, several state-of-the-art ADP approaches are reviewed in this paper. First, two model-based offline iterative ADP methods including policy iteration (PI) and value iteration (VI) are given, and their respective advantages and shortcomings are discussed in detail. Second, the multistep heuristic dynamic programming (HDP) method is introduced, which avoids the requirement of initial admissible control and achieves fast convergence. This method successfully utilizes the advantages of PI and VI and overcomes their drawbacks at the same time. Finally, the discrete-time optimal control strategy is tested on a power system.

Optimization and control of cyber–physical power systems under dual-network interactive cascading failure

Due to deep integration between power grids and communication networks, cascading failure is spreading more readily in cyber–physical power systems (CPPS), even leading to blackouts. This paper mainly investigates optimized control under dual-network interactive cascading failure to support the operation of CPPS. Firstly, the topological and interactions characteristics between power grids and communication networks are analyzed, and a CPPS model is established based on the concepts of complex networks where the correlation of nodes is described by ”degree to degree”. Secondly, considering communication constraints including simultaneous bandwidth consumption and delay, an optimal routing policy with constraints based on publish–subscribe network (PSN) architecture is employed to reconstruct the transmission paths of corrupted data packets during cascading failure. Furthermore, a round trip time is defined to determine the received states of data packets and to reflect the impact of packets delay on cascading failure. Thirdly, according to direct current (DC) power flow and optimal routing policy with constraints, an optimization and control algorithm under the interactive cascading failure is proposed to provide optimized control strategy of CPPS. Finally, simulation results demonstrate the effectiveness of the proposed method in comparison with other approaches.

Analysis of cascading failures of power cyber-physical systems considering false data injection attacks

This study considers the performance impacts of false data injection attacks on the cascading failures of a power cyber-physical system, and identifies vulnerable nodes. First, considering the monitoring and control functions of a cyber network and power flow characteristics of a power network, a power cyber-physical system model is established. Then, the influences of a false data attack on the decision-making and control processes of the cyber network communication processes are studied, and a cascading failure analysis process is proposed for the cyber-attack environment. In addition, a vulnerability evaluation index is defined from two perspectives, i.e., the topology integrity and power network operation characteristics. Moreover, the effectiveness of a power flow betweenness assessment for vulnerable nodes in the cyber- physical environment is verified based on comparing the node power flow betweenness and vulnerability assessment index. Finally, an IEEE14-bus power network is selected for constructing a power cyber-physical system. Simulations show that both the uplink communication channel and downlink communication channel suffer from false data attacks, which affect the ability of the cyber network to suppress the propagation of cascading failures, and expand the scale of the cascading failures. The vulnerability evaluation index is calculated for each node, so as to verify the effectiveness of identifying vulnerable nodes based on the power flow betweenness.