IET Cyber-Physical Systems: Theory and Applications最新文献

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.

Distributed controllers play a prominent role in electric power grid operation. The coordinated failure or malfunction of these controllers is a serious threat, where the resulting mechanisms and consequences are not yet well-known and planned against. If certain controllers are maliciously compromised by an adversary, they can be manipulated to drive the system to an unsafe state. The authors present a strategy for distributed controller defence (SDCD) for improved grid tolerance under conditions of distributed controller compromise. The work of the authors’ first formalises the roles that distributed controllers play and their control support groups using controllability analysis techniques. With these formally defined roles and groups, the authors then present defence strategies for maintaining or regaining system control during such an attack. A general control response framework is presented here for the compromise or failure of distributed controllers using the remaining, operational set. The SDCD approach is successfully demonstrated with a 7-bus system and the IEEE 118-bus system for single and coordinated distributed controller compromise; the results indicate that SDCD is able to significantly reduce system stress and mitigate compromise consequences.

While most existing works ignore securing the communication of control signals in microgrids' centralized secondary frequency control, here, a stochastic game between the microgrid control centre (MGCC) and the attacker for enhancing the vulnerability of the MGCC to false data injection (FDI) attack (wireless spoof attack) is proposed. The vulnerability to wireless spoof attack is assessed based on the controllability Gramian when the FDI attack is modelled as the malicious control input that aims to drive the microgrid state to undesired values. In the formulated zero-sum two-player Markov game, the state is uniquely associated with the vulnerability index defined by the trace of the controllability Gramian with respect to the attack input. Moreover, the utility function of the stochastic game includes not only the costs related to conducting spoof attack and encryption actions but also the possible remedy costs associated with the resulted vulnerability levels. In turn, the potential impacts of the cyber-layer action choices on the performance of the physical power system are considered when designing the optimal attack and defence strategies. A distribution feeder system with four distributed generators (DGs) is used for simulation studies. The vulnerability assessment results show that the vulnerability level increases when the attacker compromises more on the communication links between the MGCC and DGs. In the simulated game, mixed stationary attack and defence strategies are predominate when considering the uncertainty of the other player.

Energy internet (EI) can alleviate the arduous challenges brought about by the energy crisis and global warming and has aroused the concern of many scholars. In the research of EI control systems, the access of distributed energy causes the power system to exhibit complex nonlinearity, high uncertainty and strong coupling. Traditional control and optimization methods often have limited effectiveness in solving these problems. With the widespread application of distributed control technology and the maturity of artificial intelligence (AI) technology, the combination of distributed control and AI has become an effective method to break through current research bottlenecks. This study reviews the research progress of EI distributed control technologies based on AI in recent years. It can be found that AI-based distributed control methods have many advantages in maintaining EI stability and achieving optimal energy management. This combination of AI and distributed control makes EI control systems more intelligent, safe and efficient, which will be an important direction for future research. The purpose of this study is to provide a reference as well as useful research ideas for the study of EI control systems.

Cyber-physical systems (CPSs) are embodied systems of highly unified computational, control and communicational elements tightly fused with the physical world. Normally, CPSs are seen to have limited storage and computational abilities due to the fact that they are implemented across several platforms and also embedded into larger systems. The fusion of Cloud computing and CPS gives rise to Cloud-based CPS; Cloud computing will no doubt provide numerous opportunities for CPSs to increase their capabilities by taking advantage of the resources (applications, servers, storage and network capabilities). With this new addition, there will definitely be an increase in energy consumption in the system and this becomes a huge and daunting task that needs to be overcome in actualising this goal. Here, the energy consumption in the network system is being evaluated to ensure an effective data transmission amongst sensor nodes. A simulation environment is considered where a particle swarm optimization algorithm is introduced to optimise and balance the consumption of energy in the system network. To ensure an energy-efficient Cloud data control center, an energy consumption model is developed based on resource utilisation. This, however, assists in managing the amount of resources to be utilised for a specific amount of workload.

Distributed deep learning is an important and indispensable direction in the field of deep learning research. Earlier research has proposed many algorithms or techniques on accelerating distributed neural network training. This study discusses a new distributed training scenario, namely data isolated distributed deep learning. Specifically, each node has its own local data and cannot be shared for some reasons. However, in order to ensure the generalization of the model, the goal is to train a global model that required learning all the data, not just based on data from a local node. At this time, distributed training with data isolation is needed. An obvious challenge for distributed deep learning in this scenario is that the distribution of training data used by each node could be highly imbalanced because of data isolation. This brings difficulty to the normalization process in neural network training, because the traditional batch normalization (BN) method will fail under this kind of data imbalanced scenario. At this time, distributed training with data isolation is needed. Aiming at such data isolation scenarios, this study proposes a comprehensive data isolation deep learning scheme. Specifically, synchronous stochastic gradient descent algorithm is used for data exchange during training, and provides several normalization approaches to the problem of BN failure caused by data imbalance. Experimental results show the efficiency and accuracy of the proposed data isolated distributed deep learning scheme.

The penetration of renewable energy sources (RES) in distribution systems has entailed heavy deployment of monitoring and control infrastructure in the distribution system. This deployment of sensors and communication infrastructure has led to the emergence of a modern grid with a complex cyber-physical network. On one hand, the cyber-physical system can play a pivotal role in coordination and control of distribution systems through distribution system state estimation (DSSE). On the other hand, the risk caused by cyber-attack has emerged as a major challenge for grid operation. The modelling aspects, namely DC loads, plug-in-vehicle and distributed generators (DGs) in DSSE and security aspects in cyber-physical distribution systems, are highlighted herein. This paper is divided into different sections emphasising on the problems and challenges associated with DSSE, cyber-attack focussing primarily on impact of false data injection (FDI) attack and co-simulation platform for investigation of vulnerability of cyber-physical systems. A comparative study among various DSSE and challenges namely, pseudo-measurement, time synchronisation and communication issues are discussed. The potential investigation of FDI and need for a co-simulation platform in distribution systems for risk analysis are also addressed. The opportunities and future research in the field of cyber-physical distribution systems are discussed in depth.

A cyber-physical authentication strategy to protect power system infrastructure against false data injection (FDI) attacks is outlined. The authors demonstrate that it is feasible to use small, low-cost, yet highly attack-resistant security chips as measurement nodes, enhanced with an event-triggered moving target defence (MTD), to offer effective cyber-physical security. At the cyber layer, the proposed solution is based on the MULTOS Trust-Anchor chip, using an authenticated encryption protocol, offering cryptographically protected and chained reports at up to 12/s. The availability of the Trust-Anchors allows the grid controller to delegate aspects of passive anomaly detection, supporting local as well as central alarms. In this context, a distributed event-triggered MTD protocol is implemented at the physical layer to complement cyber side enhancement. This protocol applies a distributed anomaly detection scheme based on Holt-Winters seasonal forecasting in combination with MTD implemented via inductance perturbation. The scheme is shown to be effective at preventing or detecting a wide range of attacks against power system measurement system.

This paper presents the design and implementation of a Time-Sensitive Networking (TSN) protocol-enabled synchronized measurement-based monitoring system for microgrids. The proposed approach synchronizes and prioritizes the communication nodes, allowing it to transfer ultra-high three-phase sampled data and phasors. TSN is achieved by Quality of Service (QoS) profile software library. This allows control, monitoring, traffic scheduling, and prioritization. Some buses in a microgrid may have priority over others; and this can be prioritized at the data level too, where a part of the information is more critical than the others. The advantages of utilizing the TSN protocol on a microgrid with the approach proposed are: it is an alternative to GPS technology, three-phase data can be exchanged at much faster rate and data traffic in the network can be shaped with low packet loss, and low latency, in addition to providing interoperability through Data Distribution Services (DDS). These enhancements improve the communication reliability and enable distributed control, resulting in avoidance of any bottlenecks in the communications network. This proposed approach is implemented and demonstrated in a laboratory-scale microgrid. The results obtained, verify low latency and high throughput of the entire system while meeting the TSN and QoS requirements.

With the many characteristics of cyber–physical systems (CPS), including the complex evolution of various dynamic processes in distribution networks (DNs), communication modes and serious network security events have become important issues. From the perspective of DN analysis, traditional electrical digital simulations cannot access current models of communication systems and cyber network security events. The interactions between cyber and physical networks cannot be simulated and analysed simultaneously, which is challenging to the development of CPS simulations of DNs. To evaluate the impact of cyber networks on physical networks, a CPS simulation platform that contains physical, communication and control layers is discussed. Three-phase short-circuit fault processing and islanding operation control are simulated by data interactions among three-layer models that are used to analyse the impact of communication data tampering on fault handling processes. The influence of data tampering on fault processing and the influence of false data injection and data interception on islanding operation control processes are analysed. The simulation results show that the network information security has a large impact on fault protection and islanding operation control, which leads to the expansion of the outage scope and the failure of islanding operation frequency adjustment. This article also provides a simulation basis for the analysis of CPS operation characteristics and the identification and prevention of cyber network security events.