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

The ability to reduce emissions and improve sustainability in agricultural consumer electronics has been significantly hindered due to the use of energy-intensive technology within the agricultural sector. This study proposes a new enhancement of deep Q-learning (DQN) with principal component analysis (PCA) focused on energy efficiency. PCA helps manage massive operational data by performing dimensionality reduction, whereas DQN, a reinforcement learning paradigm, optimises decision-making during real-world interactions. The main contribution of this study is in the combined use of PCA and DQN to form customisable, precise, contest-responsive energy frameworks powered by real-time analytics on agricultural data—energy management on such a scale has not been approached in the context of sustainable agriculture before. The experiments confirm the optimal model, further achieving a cumulative reward of 72.56, an average emission of 1.83, a Q-value of 24.76 and a total zenith value of 75.40% in ensuring numerous noncriteria-defined efficient energy-dependent operations. This paradigm not only fills the void in the automation of passive intelligent agricultural systems but also serves as a point of reference for other eco-critical domains to strive towards greener technology.

This paper focuses on the development of cyber-resilient gradient-based optimisation algorithms and theoretical proof for grid-interactive distributed energy resource (DER) control to enable two grid services of virtual power plants (VPPs) dispatch and grid voltage regulation, considering the communication and security impacts. Firstly, the combined DER dispatch and voltage regulation as a real-time gradient-based optimisation problem is recapped. Thereafter, we consider a probabilistic traffic model to characterise packet delays and loss in a communication network, and study how the delays enter the process of information exchange among the grid measurement units, local DER controllers and the grid control centre that execute this control algorithm in a coordinated manner. Then, a strategy combining delay thresholds and message update rules is proposed to immunity the asynchrony resulting from the communications traffic and it avoids possible numerical instabilities and sensitivities of the power tracking and voltage regulation capabilities, resulting as cyber-resilient DER control algorithms. Additionally, their convergence is theoretically proved. Effectiveness of proposed cyber-resilient algorithms has been validated on the IEEE 37-bus system in terms of convergence, VPP tracking and voltage regulation performance for smart distribution systems with high penetration of DERs.

Fault-diagnosis methods based on deep learning technology have been widely applied in gear fault diagnosis. Gearboxes often operate under complex and harsh conditions, which can lead to faults. Therefore, monitoring the condition of gearboxes and diagnosing faults are crucial for ensuring the reliability and safety of the system. In response, this paper proposes a gear fault diagnosis model based on the adaptive prototype hashing (APH) optimisation algorithm for diagnosing faults in rotating machinery. This method combines the advantages of adaptive prototype hashing with transformers to improve the accuracy of fault diagnosis. The model utilises an adaptive prototype selection mechanism to dynamically select the most representative samples as prototypes and employs the transformer model to extract feature representations of the input data. In classification tasks using two datasets, the model achieved an accuracy of 98.11% under normal conditions. In experiments with added white noise and a smaller sample size, the accuracies reached 96.81% and 86.41%, respectively. Additionally, we conducted ablation experiments with advanced transformer models, where the APHformer model incorporating the APH layer achieved fault diagnosis accuracies exceeding 97%, significantly outperforming other combinations. Furthermore, T-SNE visualisation results indicate that the method performs well in feature representation. This study provides important insights into the field of gear fault diagnosis based on deep learning and has potential practical application values.

The increasing penetration of distributed renewables creates new threats to the optimal planning, management, and operation of the electric grid. In particular, new standards that mandate real-time visibility and communications to grid operations, coupled with supply-constrained inverter manufacturers, have exposed the electric grid to increased cyber risk and challenges to resiliency. Despite these developments, the electric industry must fully understand the emerging threats and develop a comprehensive and balanced approach to mitigation of the cyberattack risk by grid operators while maintaining the overall grid resiliency that connected and communicating renewables can provide. This study explores the impact of a coordinated cybersecurity attack on distributed renewables in the electric grid and proposes a novel approach to reduce the disruption of services to customers. This approach is anchored on a shift from centralised multi-party control to decentralised node-based control. It provides a starting point to address the overall framework for connecting, controlling, and securing distributed renewables, which can improve cybersecurity protection levels while maintaining the reliability of connected energy assets.

Traditional integrated energy management systems may lack comprehensive scheduling and management strategies for wind, solar and natural gas energy storage. This may lead to imbalanced utilisation of energy and the inability to fully utilise the advantages of various energy sources, thereby affecting the economy and operational efficiency of the system. A transient synchronous stability control method for wind, solar and natural gas energy storage integrated energy management systems considering carbon constraints and dynamic characteristics is proposed. Firstly, with the optimisation objective of system economy, a combined dynamic stability analysis method for photovoltaic panels, wind turbines and gas turbines is proposed based on the carbon constraints and dynamic characteristics distribution of wind, solar, gas and energy storage integrated energy management systems. A comprehensive energy management rule model for wind, solar and natural gas storage is established. This comprehensive energy management rule model can help the system achieve comprehensive scheduling and management of wind, solar and natural gas energy storage, in order to maximise the economic and operational efficiency of the system. Then, the model was analysed based on the characteristics of total fuel consumption and unit fuel price. The operational cost is used to describe the lifecycle control project of the material price management system and a control project is constructed with the goal of integrating the annual total cost of the energy system. Finally, the grid search algorithm is used to find the optimal combination of optimisation variables. This model uses transient synchronous control variables for optimisation and solution, such as system radiation conditions, wind conditions, stepped electricity pricing system loads and equipment parameters. Realise transient synchronous and stable control of the integrated energy management system of wind, light, gas and energy storage. The simulation results show that the WS-G-EMS transient synchronisation control using this method has good stability and excellent performance with good stability and small convergence error.

Wide-area voltage control system (WAVCS) ensures comprehensive voltage security and optimal management of power resources by incorporating flexible alternating current transmission system (FACTS) devices. However, due to its reliance on a wide-area communication network and coordination with FACTS-based local controllers, WAVCS is susceptible to cyberattacks. To address this issue, we propose a data-driven attack-resilient system (DARS) that integrates a machine learning-based anomaly detection system (ADS) and rules-based attack mitigation system (RAMS) to detect data integrity attacks and initiate necessary corrective actions to restore the grid operation after disturbances. The proposed ADS utilises the variational mode decomposition (VMD) technique to extract sub-signal modes from the measurement signals of WAVCS and computes statistics features to detect data integrity attacks using machine learning algorithms. Our proposed methodology is evaluated by emulating the fuzzy logic-based WAVCS, as developed by the Bonneville Power Administration (BPA), for Kundur's four machine two-area system. The WAVCS applies $��V�$ mag$��Q�$ algorithm that utilises synchrophasor measurements (voltage magnitude and reactive power) to compute an optimal set-point for FACTS devices. Experimental results show that our proposed algorithm (VMD-DT) with statistics features outperforms existing machine learning algorithms while exhibiting a smaller processing time. Also, the proposed RAMS is effective in maintaining transient voltage stability within acceptable voltage limits by triggering different modes of operations upon detection of anomalies in grid network.

To ensure normal and efficient operation, microgrids (MGs) must fully integrate information and communication technologies into their control systems. However, this integration introduces vulnerabilities to cyberattacks that can compromise sensitive data and disrupt operations. In MGs, two distinct types of data communication flows pose cybersecurity risks. The first involves local data transfers, which occur directly between devices based on their MAC addresses. The second involves network-wide data transmission using the Internet protocol (IP). When assessing the potential impact of cyberattacks on control systems, it is crucial to consider the specific nature of the protocols in use. This research analyses the impact of cyberattacks on data transmission in MGs and develops appropriate models for well-known attack types. It introduces updated models for false data injection (FDI) and denial of service (DoS) attacks to evaluate their impact on numerical stream data in MGs. Experimental and simulation-based validations are conducted to develop accurate cyberattack models and support the design of resilient control systems. The vulnerability of MGs' secondary control to these cyberattacks is assessed through MATLAB simulations. The results indicate that the impact of each attack depends on factors such as the packet sampling time, the injected data values (for FDI attacks) and the induced delays (for DoS attacks).

Cyber-physical systems (CPS) serve as the backbone of critical infrastructure, seamlessly integrating computation, networking and physical processes. However, the growing interconnectivity of these systems also increases their exposure to sophisticated cyber threats. Ensuring the security and privacy of CPS is crucial to maintaining operational stability, preventing service disruptions and mitigating cascading failures.

This Special Issue presents cutting-edge research addressing diverse aspects of CPS security, ranging from attack methodologies to vulnerability assessments and resilience strategies. The selected papers provide insights into real-time attack implementations, advanced analytics using graph theory, multi-stage cyber threat scenarios and socio-technical security modelling.

In this Special Issue, we have received 7 papers, all of which underwent peer review. Of the six originally submitted papers, four have been accepted and two have been ‘rejected with referral’, that is, they did not meet the criteria for publication to the IET Cyber-Physical Systems: Theory & Applications, and on. Thus, the overall submissions were of high quality, which marks the success of this Special Issue.

The four eventually accepted papers can be categorised into four key themes: (1) cyber-physical attack modelling and system vulnerability, (2) graph-based cyber-physical system security analysis, (3) multi-stage cyber threats and impact assessment and (4) socio-technical security modelling for cyber-physical systems.

Yadav et al. investigate the impact of sliding mode-based switching attacks on power system components. By leveraging real-time simulation techniques, the study highlights how cyber-attacks on circuit breakers, excitation systems and governors can lead to cascading failures. The results offer valuable insights into the vulnerabilities of power grids and the need for proactive mitigation measures.

Jacobs et al. introduce a novel graph clustering approach for analysing cyber-physical interactions in smart grid environments. The study demonstrates how clustering techniques can help characterise disturbances, identify critical system components and enhance situational awareness. These findings pave the way for improved cybersecurity strategies by enabling better detection and response mechanisms.

Al Homoud et al. present an in-depth case study on a multi-stage cyber threat targeting power systems. The research details how cyber intrusions can escalate, leading to severe physical consequences in the grid. By leveraging the MITRE ATT&CK framework, the authors propose defence strategies that enhance the resilience of cyber-physical energy management systems.

Ani et al. explore socio-technical security modelling and simulation (STSec-M&S) in cyber-physical systems (CPS) to enhance critical infrastructure (CI) cybersecurity, emphasising its potential for integrating technical and social aspects to

Wind energy plays an essential position in the renewable energy sector and is frequently deployed remotely, which makes them susceptible to intrusions that can compromise their operational system. This paper introduces a novel method T–IDS leveraging the interconnected thermal behaviours of wind turbine modules to identify the abnormal imprints that signify security breaches. Our approach consists of three key components: a graph model that outlines the dependencies among the thermal variables of the turbines, a random forest-based prediction strategy for these variables within the thermal graph and an anomaly detection method that assesses the predicted thermal values with actual observations. We performed extensive experiments using three real-world wind turbine supervisory control and data acquisition (SCADA) log datasets: one dataset collected over six months and two additional datasets covering 12-month operational durations from distinct wind turbine installations for rigorous cross-validation. The results demonstrate that T–IDS achieves an overall anomaly detection accuracy of 97.3% when detecting unusual thermal activities such as physical model damage leading to overheating or tampering temperature readings.

Advancement in wireless and communication technologies has remarkably boosted healthcare services such as Medical WSN. Protecting the patient's health-related data against malicious activities is also essential. It is mandatory to ensure its dependability and reliability. The reliability of the proposed model in Secure Multimedia Transmission for medical wireless sensor network (IRSMT) system considers the need for authentication and confidentiality in security. Additionally, it enhances the transmission reliability during multimedia data transmission compared to the prevailing methods. To provide reliable multimedia (MM) data transmission, an improved energy-efficient protocol is considered where the protocol differentiates MM and non-MM data to enhance routing methodology for MM transmission. The proposed IRSMT enhances adaptability by balancing media quality with prompt delivery and loss tolerance. It is achieved through the anonymous routing method, which maintains the node secrecy using the SHA 256 method. It reduces the probability of data retransmission and provides less processing delay to acquire routing reliability. The simulation results demonstrate the advantages of IRSMT in comparison with the prevailing protocols in performance metrics such as throughput, packet delivery ratio, jitter etc.