Cyber Layer Research Articles

Real-time Volt-Var control of grid forming converters in DER-enriched distribution network

The growing installation of distributed energy resources (DERs) in a distribution network (DN) poses substantial issues related to voltage regulation. Due to constrained switching operation and slower response time, traditional voltage regulation devices cannot handle current voltage-related challenges. One alternative to solve these problems is to use smart converters to control the reactive power to regulate the voltage. Volt-Var control (VVC) is one of the simplest approaches for controlling the reactive power from smart converters. Among several converters, grid forming converters (GFCs) are more suitable in DER-enriched distribution networks. Since DER-enriched distribution networks have a higher fluctuation in voltage profile, real-time control is advantageous. Therefore, this work presents an advanced real-time reactive power control for handling voltage violations in a DN using GFC. The uniqueness of this method is that it controls the voltage magnitude of affected nodes by dispatching reactive power from smart converters in real-time. By running cyber-physical co-simulation (CPCS) between the Typhoon HIL 604 and OpenDSS, the Volt-Var control can be done in real time. The grid-forming converter is modelled in Typhoon HIL 604, which acts as a physical layer of the proposed cyber-physical system for real-time VVC. A CIGRE medium voltage distribution network is designed in OpenDSS and serves as one of the parts of the cyber layer. The CPCS between Typhoon HIL and OpenDSS and the control algorithm are both done by a programme written in Python. The execution of the control algorithm is performed in real time using the Supervisory Control and Data Acquisition (SCADA) developed in this study. The real-time simulation shows that the proposed real-time VVC is capable of handling voltage violations in real time in DER-enriched distribution networks.

A Cross-Layer Design Approach to Strategic Cyber Defense and Robust Switching Control of Cyber-Physical Wind Energy Systems

Due to the increasing adoption of smart sensing and Internet of things (IoT) devices, wind energy system (WES) becomes more vulnerable to cyber and physical attacks. Therefore, designing a secure and resilient WES is critical. This paper first proposes a system-of-systems (SoS) framework for the cyber-physical WES. Specifically, on the one hand, we adopt a game-theoretic model to capture the interactions between the WES system defender and the adversary at the cyber layer. The outcome of this cyber defense game is reflected by control-aware Nash equilibria. On the other hand, we devise a cyber-aware robust and resilient switching controller based on a Markov jump linear system model for the physical WES. The performances of the WES cyber and physical layers are interdependent due to their natural couplings. We further investigate the SoS equilibrium of the integrated WES, which considers the system security, robustness, and resilience holistically. Finally, we use case studies to corroborate the developed cross-layer design principles for the cyber-physical WES. Note to Practitioners—Cybersecurity becomes a critical concern of wind energy system (WES) operators as an increasing amount of IoT devices are adopted for WES’s communication, monitoring, and operation support purposes. This cyber-physical integration in WES creates a much broader attack surface because adversaries can compromise the physical WES by attacking its dependent cyberspace. To mitigate the impact of attacks, the operator should not only design intelligent control strategies for WES but also strategically secure the WES’s cyber layer. These two goals are naturally coupled together. On the one hand, the WES operates under different compromised conditions depending on the attack actions at the cyber layer. Thus, the control design needs to be adversary-aware by taking the real-time cyber state into account. On the other hand, the adversary’s cyberattack strategy is influenced by the induced performance degradation of WES. Hence, the corresponding attack measures and countermeasures, in turn, should be physically control-aware. This paper establishes a holistic mathematical framework to simultaneously address these two challenging objectives. The obtained solution provides guidelines for the WES operator on the optimal security resource investment in defending against cyberattacks and the robust switching control design to mitigate the impacts of attacks further. This methodology creates a defense-in-depth paradigm for the WES operators to maintain the energy system efficiency in the adversarial environment. This cross-layer design approach is also efficient and user-friendly for online implementation with the developed iterative algorithm. The simulated-based case studies in this paper show the effectiveness of the proposed approach. However, a more thorough validation of the method in practice is necessary before its integration with the production standard.

Digital twin architecture and sim-to-real gap analysis of a material transfer system in a remanufacturing environment

This paper presents a digital twin architecture for a material flow system as well as experiments to determine the sim-to-real gap between virtual models and their real counterparts. The architecture comprises three layers, namely the physical/logical layer, the cyber layer as well as the descriptive layer. Virtual and real assets are able to communicate on the cyber layer by means of OPCUA and the Asset Administration Shell (AAS). The virtual models are implemented in a physics-based simulation environment and allow the reuse of all control and application layer software modules of their real counterpart. In addition, the sim-to-real gap between a selected asset, namely the Transfer Unit is analyzed by means of a collision analysis of the material transfer of simple shaped objects. The results show similar behavior of the real and the virtual models. However, more effort is needed to accurately model the meshes of the collision elements to ensure realistic results.

Multi-Quadrotor Formation Tracking with Mixed Constraints: A Hierarchical Rolling Optimization Approach

This paper investigates the formation tracking problem of multi-quadrotor unmanned aerial vehicles (UAVs) that are subject to mixed constraints including practical constraints on the velocities, attitudes and input forces. These constraints present significant challenges in regulating and controlling the UAVs. We propose a hierarchical control strategy with a local layer based on model predictive control (MPC) and a cyber layer based on cooperative rolling optimization control (CROC) to simultaneously solve the tracking problems and ensure that corresponding physical states conform to the mixed constraints. In the cyber layer, each node only needs to receive the information from its neighbor nodes to perform synchronous rolling optimization. The cost function of the local open-loop rolling optimization problem is constructed by penalizing the estimated predicted (E-predicted) trajectory and the estimated assumed (E-assumed) trajectory error in the cyber layer. In the local layer, we propose a control algorithm to solve the mixed constraint problem of the cascade control of the quadrotors, which consists of inner loop attitude control and outer loop trajectory tracking control. Finally, the simulation results are presented to analyze the performance and the effectiveness of the proposed algorithm.

A Cross-Layer Defense Method for Blockchain Empowered CBTC Systems Against Data Tampering Attacks

Due to the high integration of wireless communication and networking technologies, the communication-based train control (CBTC) systems are exposed to additional cyber-attack surfaces, allowing sophisticated attackers to combine cyber attack vectors with physical attack means to achieve malicious goals. Notably, the decentralized authentication features are missing in existing communication protocols which make the CBTC be easily compromised by data tampering attacks, and lead to serious operational accidents. With outstanding advantages in decentralized authentication, blockchain provides new effective solutions for decentralized identity authentication in CBTC. Consequently, it is critical to study the complex physical consequences of cyber breaches from a cross-layer defense perspective. In this paper, we propose a novel cross-layer defense method for cyber security in blockchain empowered CBTC against data tampering attacks. In the physical layer, the joint Kalman filter and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\chi ^{2} $ </tex-math></inline-formula> detector is proposed for the train state estimation and detection. In the cyber layer, an asymmetric encryption-based secure communication protocol with identity authentication and the blockchain-based distributed key management system with the adaptive consensus mechanism are designed for data communication security. Considering the unavailable direct observation of the CBTC cyber security states, a partially observable Markov (POMDP) decision model is constructed to derive the optimal adaptive consensus strategies for balancing cyber security and efficiency. Extensive simulation results show that the proposed blockchain empowered CBTC cross-layer defense method can effectively improve the cyber security protection capability and minimize the impact of data tampering attacks on the train operation.

Model Predictive Control for Networked Multiple Linear Motors System Under DoS Attack and Time Delay

The complex cyber layer challenges the accuracy of cooperative tracking motion for the networked multiple linear motors system. This article focuses on model predictive control (MPC) for networked control system with the denial-of-service (DoS) attack and network-induced time delay. In order to improve tracking accuracy and control performance of the system, several topologies for linear motion models are studied. The DoS attack and time delay are modeled as constraint models in the time domain. Combining dynamic prediction horizon with the buffer compensation in the controller to actuator (C-A) channel, the proposed networked control strategy based on MPC effectively solves the impact of DoS attacks and time delays on the tracking performance of multiple linear motors. In order to ensure the recursive feasibility of the MPC optimization problem and the uniform global asymptotic stability of system, the output terminal constraint and dynamic prediction horizon constraint are considered by combining four DoS attack cases of the networked control strategy, and a Lyapunov function is constructed. Finally, three linear motors with different topologies are built, and the simulation results verify the control effectiveness of the proposed control strategy on the motor tracking performance under cyber constraints.

A distributed compensating strategy for power systems pre-protection against disruptive contingencies using heterogeneous FACTS devices

The responsive and effective pre-protective actions are vital to the resilience of electrical power systems. This paper addresses the online pre-protection problem of power systems subject to disruptive disturbances caused by contingencies. A novel distributed optimization algorithm for flexible alternate current transmission systems (FACTS) devices is proposed to adjust the branch impedance and voltage phase angle with the aid of thyristor-controlled series compensation (TCSC) and thyristor-controlled phase shifting transformer (TCPST), which enables to regulate power flow and eliminate the disruptions caused by bus overloads and transmission line faults. Moreover, theoretical analysis is conducted to guarantee the convergence of numerical algorithm for solving the distributed optimization problem. Finally, numerical simulations are carried out to validate the proposed optimization algorithm on IEEE 9 bus system, and a performance index is proposed to better compare control effects with existing centralized control algorithm. • Create a novel network model for power grids to decouple the physical layer and cyber layer. • Develop a distributed optimization algorithm by integrating FACTS devices for the real-time protection of power systems. • Provide rigorous theoretical analysis for the distributed optimization algorithm with guaranteed convergence.

Data-Driven Detection of Stealth Cyber-Attacks in DC Microgrids

Cyber-physical systems such as microgrids contain numerous attack surfaces in communication links, sensors, and actuators forms. Manipulating the communication links and sensors is done to inject anomalous data that can be transmitted through the cyber layer along with the original data stream. The presence of malicious, anomalous data packets in the cyber layer of a dc microgrid can create hindrances in fulfilling the control objectives, leading to voltage instability and affecting load dispatch patterns. Hence, detecting anomalous data is essential for the restoration of system stability. This article answers two important research questions: 1) Which data-driven detection scheme offers the best detection performance against stealth cyber-attacks in dc microgrids? 2) What is the detection performance improvement when fusing two features (i.e., current and voltage data) for training compared with using a single feature (i.e., current)? Our investigations revealed that 1) adopting an unsupervised deep recurrent autoencoder anomaly detection scheme in dc microgrids offers superior detection performance compared with other benchmarks. The autoencoder is trained on benign data generated from a multisource dc microgrid model. 2) Fusing current and voltage data for training offers a 14.7% improvement. The efficacy of the results is verified using experimental data collected from a dc microgrid testbed when subjected to stealth cyber-attacks.

Protection of Water Distribution Networks against Cyber and Physical Threats: The STOP-IT Approach Demonstrated in a Case Study

Water critical infrastructures are undergoing a process of digital transformation that entails an increasing integration between the physical and cyber layers of the system. This integration brings efficiency and monitoring advantages, but it also exposes water systems to a new threat surface that includes cyberattacks. Formed in 2017, STOP-IT is Europe’s first project dedicated to developing cyber-physical security solutions tailored to the water sector. During the 4 years of collaboration, the STOP-IT team has codeveloped an extensive list of technologies that integrates cyber and physical layers of infrastructure, allowing water utilities to prevent, detect, assess, and treat risks, as well as simulate scenarios of attacks and explore how to react to increase preparedness. This article first introduces the overall aim and main outcomes of the STOP-IT project and then focuses on the risk management integrated framework composed of modeling solutions developed to help water utilities identify vulnerabilities and protect critical parts of their systems. The solutions are presented along with the results from the demonstration activities performed by a selected water utility concerning three risk scenarios that were assessed through the mentioned integrated framework.

A novel data-driven time-delay attack evaluation method for wide-area cyber–physical smart grid systems

One of the main challenges in information and communication technology, as well as wide-area power system researches, is the inability to stabilize the voltage and frequency for cyber–physical power systems under the communication time-delay. Thus, it is imperative to investigate the stochastic characteristics and quantitative method of time-delay. In this paper, a novel data-driven time-delay evaluation method for cyber–physical smart grid systems is proposed. The cyber layer and physical components in wide-area power systems are modeled. The transmitted process of the data packet in a cyber layer is considered, and the probability distribution function of multiple latencies in the spatial sequence is depicted by using M/M/1 queuing theory and signal convolution methods. The Sklar’s theorem and Copula’s theory are utilized to integrate distribution functions of multiple delays into a novel delay quantitative model. Furthermore, the time delay is divided according to the reason of its generation as two parts: (1) inherent delay by nature, and (2) delay caused by network attacks. Various sizes of delays caused by cyber-attacks are detected by using communication principle and likelihood ratio. Finally, the case studies under various scenarios are carried out on the New England ten-machine power system. The simulation results show that the stochastic latencies obey the proposed delay distribution model with probability 99.9%. The comparative results illustrate the mean-square error between the proposed and actual delay distribution probability function as 10−4. The delay caused by cyber-attacks can be separated from nature latency with the threshold η=0.0101.

Cooperative braking of urban rail vehicles with Koopman model predictive control

AbstractBraking control of urban rail vehicles with multiple carriages is critical to ensure the safe operation of urban rails. However, existing decentralized braking control methods lead to the inconsistent speed and excessive coupler force among carriages, which compromises the operation safety of urban rails. To address this issue, a cooperative braking strategy is proposed for urban rail vehicles based on Koopman model predictive control method. First, a cyber‐physical model is established, where the physical layer characterizes the dynamic model of multiple carriages using the Koopman operator. The cyber layer represents the communication topology of carriages with graph theory. Second, a cooperative braking controller is designed with the distributed model predictive control method. An optimisation problem is formulated to minimize the speed inconsistency and relative displacement difference among carriages. Third, extensive simulations under various scenarios, including normal and extreme operating conditions, are conducted to verify the effectiveness of the proposed method. Simulation results show that, compared with the classical decentralized method, the proposed braking method reduces the average relative displacement by 78% while reducing the speed error by 29%.

A Novel Approach for Estimating Performance of IIoT-Based Virtual Control Train Sets under DoS Attacks

The virtually coupled train sets (VCTS) have been proposed to improve operational capability and passenger satisfaction and ensure punctuality, thus alleviating the rapidly worsening traffic pressure. Recently, due to the lack of reliable wireless communications and accurate perceptual information, VCTS based on industrial internet of things (IIoT) are receiving growing concerns by integrating into the IIoT, AI, and edge computing. However, denial of service (DoS) attacks are feasible for IIoT-based VCTS due to the physically exposed open electromagnetic environment. They would cause severe safety and punctuality problems, such as poor real-time capability, enormous packet dropout rates, extensive train operational delays, and disturbance in the train convoy’s dynamic schedule. This paper deeply discusses the effects of DoS attacks on the performances of the IIoT-based VCTS by combing the physical layer with the cyber layer and explores the requirement of an attacker. We consider that the system is under the attack of a rational DoS attacker with limited jamming attacks, which will cause the most system state offset. In the paper, we propose a novel train status estimation approach to compensate for the losing information of the front train by the trade-off between the best gain of the DoS attacker and the punctuality of the IIoT-based VCTS. System performance includes physical dynamic indicators, train operational delay variance, and average waiting time of passengers. Taken together, these findings indicate that the established status estimation approach can effectively mitigate safety concerns and reduce train operational delays.

A Consensus-Based Distributed Two-Layer Control Strategy with Predictive Compensation for Islanded Microgrid CPS against DoS Attack

Summary.Aiming at the problems of insufficient scalability and slow response speed of the traditional three-layer control structure based on the time scale, this study proposes a distributed two-layer control structure. The primary control uses traditional power-frequency droop control, and the second-level control adopts a consensus protocol to simultaneously achieve the goals of frequency synchronization, frequency non-difference, and power optimization in a distributed manner, which can effectively improve the performance of microgrid frequency adjustment and power optimization. The cyber layer of the AC microgrid cyber-physical system (CPS) is extremely vulnerable to denial-of-service (DoS) attacks, resulting in the inability to achieve control objectives. For this reason, this paper designs a consensus algorithm based on event-triggered and a predictive compensation control link that combines empirical mode decomposition (EMD) and extreme learning machine (ELM) on the basis of the two-layer control structure. Finally, a 4-node islanded microgrid simulation example is used to verify the effectiveness of the proposed strategy. The simulation results show that the two-layer control strategy can achieve microgrid frequency recovery and power optimization while effectively responding to different degrees of DoS attacks.

Dynamic Event-Triggered Distributed Secondary Control of DC Microgrids

Emerging distributed control paradigms rely on communication among converters of a microgrid. We investigate the secondary control of dc microgrids with a distributed dynamic event-triggering mechanism. The physical and cyber layers are represented by different graph topologies. To reduce the communication burden, a distributed dynamic event-triggering mechanism is designed. Therein, the Zeno behavior is excluded and in addition, a positive minimum interevent time (MIET) is determined. Asymptotic stability of the closed-loop system dynamics is proven, and the adjustment of the positive MIET is discussed. Compared to the existing static event-triggering mechanisms, the proposed dynamic one guarantees the existence of a positive MIET, whose value can be tuned by the design parameters. Controller/hardware-in-the-loop implementation results validate the efficacy of the proposed method.

Resilient Consensus-Based AC Optimal Power Flow Against Data Integrity Attacks Using PLC

This paper investigates the resilience of consensus-based AC optimal power flow (OPF) for distribution networks against the data integrity attacks (DIA). The distribution network is first decomposed into several autonomous microgrids to achieve consensus-based AC OPF through the alternating direction multiplier method (ADMM). Then the vulnerability of consensus-based AC OPF is analyzed by showing how the dispatch results can be easily altered by attackers through falsifying the information (active power, reactive power, and voltage) shared between neighboring microgrids. Two different attack scenarios, viz., economic-driven attacks and disruptive attacks, are studied to show the malicious influence of cyber attacks on consensus-based AC OPF. A detection and mitigation strategy based on the existing power line communication (PLC) infrastructure is then described, where the critical information is not only shared in the cyber layer, but also through the power lines. Game-theoretic analysis is provided to demonstrate the effectiveness of the proposed mitigation strategy. Furthermore, to validate the authenticity of the information through PLC, a physical encryption method based on the Lorenz system is proposed. The effectiveness of the proposed attack-resilient mechanism is verified using the IEEE 123-bus test system.

Sensor fault detection and isolation via networked estimation: rank-deficient dynamical systems

This paper considers model-based fault detection of large-scale (possibly rank-deficient) dynamic systems. Assuming only global (and not local) observability over a sensor network, we introduce a single time-scale networked estimator/observer. Sensors take local outputs/measurements of system states with partial observability and share their information (including estimation and/or output) over a communication network, and gain distributed observability. We define the conditions on the network structure ensuring distributed observability and stabilising the error dynamics. However, system outputs are prone to faults and uncertainties, which affect the state estimation of all sensors as a consequence of communicating (possibly) faulty data. From the cyber-physical-systems (CPS) perspective, such faults add bias to the data transferred from the physical layer (dynamic system) to the cyber layer (sensor network). In this work, we propose a localised fault detection and isolation (FDI) mechanism at sensors to secure distributed estimation. This protocol enables every sensor to locally identify the possible fault at the sensor measurement, and, via local detection and isolation, to prevent the spread of biased/faulty information over the network. This distributed isolation and localisation of fault follows from our partial observability assumption instead of full observability at every sensor. Then, other sensors can estimate/track the system by using observationally-equivalent output information to recover for possible loss of observability. In particular, we study rank-deficient systems as they are known to demand more information-sharing, and thus, are more vulnerable to the spread of possible faults over the network. One challenge is the detection of faults in the presence of system/output noise without making (simplifying and unrealistic) upper-bound assumptions on the noise support. We resolve this by adopting probabilistic threshold designs on the residuals. Further, we show that additive faults at rank-deficiency-related outputs affect the residuals at all sensors, a consequence that mandates more constraints on the (distributed) FDI strategy. We address this problem by constrained LMI design of the feedback gain matrix. Finally, we design q-redundant distributed estimators, resilient to isolation/removal of up to q number of faulty sensors, and further, we consider thresholding residual history over a sliding time-window, known as the stateful FDI.

Security switching control of cyber-physical system with incremental quadratic constraints under denial-of-service attack

This paper presents an observer-based security control scheme for a nonlinear cyber-physical system (CPS). Specially, the nonlinearity satisfies the incremental quadratic constraints ([Formula: see text]) which can represent many types of common nonlinearities. The problems are discussed under the assumption that both the feedback and forward channels may suffer from denial-of-service (DoS) attacks. The attack model is constructed in the form of time-delayed switching system with four subsystems corresponding to different attack situations. Then by using the attack model, an observer-based controller is constructed in the cyber layer to stabilize the CPS encountering DoS attacks. The closed-loop stability is analyzed based on H∞ index of switching systems in view of average dwell time (ADT) concept. Besides, the gain matrices of the controller and the observer are obtained by solving a series of matrix inequalities using an algorithm designed in this paper. Finally, the performance of security control scheme is presented by a simulation example.

Distributed CPS-based model predictive compensator for DC microgrids with cyber-layer constraints

In this paper, we design a distributed model predictive compensator (DMPC) in the cyber layer based on the mechanism of rolling optimization to compensate the bias of the nominal voltage in the droop control. We then present the structure of cyber–physical system (CPS) to combine the DMPC with the droop control in the physical layer to form a uniform framework as well as accomplish high-quality voltage regulation and power allocation. We also deal with the case of the cyber-layer constraints since the states of cyber-layer among the distributed generators (DGs) of the microgrid are generally constrained due to their great impacts on the stability of the physical layer. Especially, the presented CPS-based DMPC is developed in a fully distributed manner, namely, only the compensated parameters of the neighbors can be accessible for each DG and no global information is employed in the whole control process. Besides, the stability of the presented DMPC can be guaranteed if the initial communication states satisfy the consistency constraint. The effectiveness and feasibility of the presented CPS-based DMPC is verified numerically in the presence of the motor faulty. It is demonstrated from the theoretical analysis and simulation results that the settling time for compensating the droop parameter is acceptable and satisfies the principle of “plug-and-play”.

Distributed carbon-aware energy trading of virtual power plant under denial of service attacks: A passivity-based neurodynamic approach

With the popularity of distributed power generation units, traditional consumers become prosumers. Meanwhile, the emerging information and communication technology brings new opportunities to the energy trading service by providing various platforms. Thus, the energy trading problem of prosumers quickly ascends to the spotlight. Nevertheless, the personalized small-scale prosumers bring challenges of coordinated operation, supply-demand balance and carbon emission control. In this paper, based on an edge-cloud platform, a distributed carbon-aware energy trading mechanism is proposed for coordinating the prosumers within the virtual power plant. Moreover, the personalized characteristics including generation cost and the carbon emissions are handled without revealing the private information under the distributed formulation. In this way, the coordination effects of the prosumers are fully explored, and a multi-agent network is employed to achieve the supply-demand balance and the carbon emission awareness. Moreover, to deal with the denial of service attacks in the cyber layer, a passivity-based neurodynamic approach is proposed, which is resilient to denial of service attacks. A set of realistic case studies on energy trading problem under denial of service attacks are given, demonstrating the effectiveness of the proposed mechanism in coordinated operation, supply-demand balancing and carbon-awareness under denial of service attacks.

Hierarchical Adaptive Control of the Cyber–Physical Power System With MMC-MTDC for Transient Stability Improvement

Large-scale wind power integration into the power system has promoted the development of a multiterminal DC (MTDC) transmission grid with a modular multilevel converter (MMC). Basically, MTDC with MMC is a typical cyber–physical system with continuous coupling interactions between cyber assets and power systems. However, cyber events may introduce many internet-based vulnerabilities and even result in the loss of transient stability of the power system. Therefore, a voltage compensation-based two-level hierarchical adaptive control strategy is proposed in this article. At the higher level, a modified MMC output current reference calculation method is developed in the αβ framework to guarantee the transient stability of the power system, whereas a feedback adjustment method is proposed in the MMC control framework, at the bottom level, to contain the controller from deviating from its output reference while eliminating the impact of cyber communication delay on transient stability. The article shows that the proposed hierarchical control strategy can improve the transient stability of the power grid under the interference of three-phase ground faults in physical and communication delays in the cyber layer. Finally, the simulation results of the modified IEEE 9-bus test system with MMC–MTDC are used to demonstrate the effectiveness of the proposed scheme.