The smart grid is an upgraded concept of electricity network with tight coupling among information, control, and bi-directional communication technologies. Along with the silent features of the on-going smart grid, cyber–physical security appears to be a deep concern due to its significant dependence on sensing technologies, complex networks of computers, intelligence, and wide-area communication infrastructures. Moreover, the smart grid is an extensive critical infrastructure and vulnerable to coordinated cyber–physical attacks. As a result, cyber–physical attacks cause significant threats to the confidentiality and integrity of the power data, including power outages, cascading failures, and unnecessary expenditure. In this study, security issues of smart grid, including cyber–physical interdependency, attack varieties, detection methods, requirements, standards, challenges, and future prospects, are taken into consideration for both cyber and physical systems, aiming to provide an extensive understanding of vulnerabilities and solutions for the smart power grid. By revealing the inherent features of cyber–physical security in the smart grid, this survey study is addressed to facilitate future research in these two areas.

With the acceleration of economic globalisation and the rapid development of network communication technology, remote monitoring and the management of ship fuel consumption have received extensive attention. Traditional fuel consumption monitoring methods are difficult to meet the growing management needs of the shipping industry due to problems such as large statistical errors and delayed information feedback. In order to better conduct energy management, equipment condition monitoring, and navigation analysis, the cyber-physical system (CPS) is deployed on ships to collect shipping data and communicate with remote monitoring centres. However, complex actual sailing conditions, sailing weather and other external factors tend to reduce the accuracy of fuel consumption data. In view of this challenge, a data-aware monitoring method for fuel consumption in ship-based CPS, named DMM, is proposed in this study. Technically, the fuel consumption index of ships is introduced firstly. Then, a fuel consumption model based on CPS is proposed, which improves the current fuel consumption model of the ship. Furthermore, the artificial neural network is employed to analyse a large amount of navigation data to get more accurate monitoring results of fuel consumption. Finally, experiments are conducted to verify the effectiveness of the authors’ proposed method.

Cyber physical system (CPS) is a complex system combining computation, network and physics; object tracking is an important application of CPS. To solve the problem that the traditional kernel correlation filtering tracking algorithm cannot recover the lost object, the authors propose a re-detection object tracking algorithm. The proposed algorithm mainly designs a new adaptive detection criterion. By comparing the value of detection criterion and the value of the experience threshold, it can be judged whether the current target is lost or not. When the object tracking fails, the proposed method can generate target candidate boxes by using the edge boxes algorithm and select the best target location by applying the non-maximum suppression and the Euclidean metric methods. In addition, a fast multi-scale estimation method and an adaptive updating method are added to the tracking procedure to further improve the overall performance of the algorithm. Experimental results show that the proposed approach has a good performance in terms of precision and success rates.

Modern power systems are extensively interlaced with data communication at various levels leading to increased vulnerability to cyberattacks at individual components as well as integrated controls. An effective cyber protection is, therefore, fast becoming an indispensable requirement for the smart grids. A false data injection (FDI) attack on load frequency control (LFC) is a stealth process, which has devastating consequences while at times may also lead to catastrophic system blackouts. This work comprises detailed analyses of the LFC system vulnerability to FDI attacks. It further develops an efficient event-triggered detection scheme to leverage the LFC protection against FDI attacks. This developed event-triggered generalised extended state observer (ET-GESO) uses Lyapunov stability analysis to derive the event-triggering condition and thereby reduce the communication burden significantly. The feasibility of the proposed ET-GESO is further studied by demonstrating its Zeno behaviour exclusion. Extensive simulation studies are performed on a peer-reported two-area power transacting system and IEEE 39-bus New England system. A comparison study between the proposed technique and reported Kalman filter-based detection scheme is also performed. Different FDI attack formulations and their detection illustrate the effectiveness of the proposed detection method.

Increasing penetrations of interoperable distributed energy resources (DER) in the electric power system are expanding the power system attack surface. Maloperation or malicious control of DER equipment can now cause substantial disturbances to grid operations. Fortunately, many options exist to defend and limit adversary impact on these newly-created DER communication networks, which typically traverse the public internet. However, implementing these security features will increase communication latency, thereby adversely impacting real-time DER grid support service effectiveness. In this work, a collection of software tools called SCEPTRE was used to create a co-simulation environment where SunSpec-compliant photovoltaic inverters were deployed as virtual machines and interconnected to simulated communication network equipment. Network segmentation, encryption, and moving target defence security features were deployed on the control network to evaluate their influence on cybersecurity metrics and power system performance. The results indicated that adding these security features did not impact DER-based grid control systems but improved the cybersecurity posture of the network when implemented appropriately.

This study proposes a scalable method to assess agent-based control concepts and deployment systems for cyber-physical electrical distribution grids. The deployment system of an agent-based control concept is the system that executes the agents and their interactions in a real implementation. Due to the increasing number of controllable loads, generators, and storage systems installed in distribution grids, scalability of control concepts and deployment systems plays a key role. Hence, the assessment method for both has to be scalable itself. For this reason, the proposed method bases on DistAIX, an open-source scalable simulation tool for cyber-physical distribution grids designed for execution on distributed computing clusters. DistAIX is extended with an interface for coupling with the deployment system of the agent-based control approach. The performance of the proposed method is evaluated for an agent-based control concept called SwarmGrid-X and the deployment system cloneMAP. The results demonstrate the feasibility of the proposed method and exemplify the functionality and scalability of SwarmGrid-X and cloneMAP.

As the worst-case interacting false data to the power system state estimation (SE), cyber data attacks can avoid being filtered out by most bad data detectors. In this study, coordinated attacks (unobservable attack and logic bomb attack) and coordinated defences (honeypot and weakening vision) are used to analyse attackers’ and defenders’ behaviours, respectively. To quantify the potential physical influences (attack-and-defence) benefits, the residual of the expected state is devised. Subsequently, a zero-sum stochastic game is utilised to model the interaction between the cyber-physical power system and the external attack-and-defence actions. This game is demonstrated to admit a Nash equilibrium and the minimax Q-learning algorithm is introduced to enable the two players to reach their equilibrium strategies while maximising their respective minimum rewards in a sequence of stages. Numerous simulations of the stochastic game model on the IEEE 14-bus system show that while resisting the isolated or coordinated attacks, the optimal coordinated defences are more effective than those of isolated attacks.

Compared with traditional distributed networks, the complex access environment, flexible access mode, massive access terminal, and data in an active distribution network will bring great security challenges to data transmission. The existing data security methods, such as access control and encryption, address the security of massive, high dimensional, and non-text data in the active distribution network. Therefore, feature selection algorithm based on rough set is first given to reduce the complexity of massive and high dimensional data. And then, based on feature selection, the authors propose a data filtering function model mining algorithm by using gene expression programming (DFFM-FSGEP). Finally, to solve the data filter function model mining of the incremental dataset, they also present an incremental mining algorithm of the filtering function model based on functional fitting (IMFFM-FF). Experimental results show that the proposed algorithm in this study can greatly reduce the complexity of experimental datasets to be processed, and compared with the other algorithms, DFFM-FSGEP has higher classification accuracy and sensitivity, and IMFFM-FF has higher classification speed and classification accuracy for incremental datasets.