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

The AC/DC hybrid microgrid, which integrates AC microgrid and DC microgrid, greatly improves the flexibility of distributed energy. However, it is imperative to acknowledge that the microgrid in question is accompanied by a significantly elevated network security risk. To solve this problem, this article proposes a false data injection attack detection and alarm method based on active power output. The detection algorithm is capable of detecting attacks at any location within the microgrid and mitigating the impact of communication delay. Moreover, a distributed elastic recovery strategy based on the aforementioned detection algorithm is proposed. This strategy aims to expeditiously restore the compromised agent to its rated state. Finally, a simulation model is developed to demonstrate the effectiveness of the proposed strategy.

Cyber-Physical Systems (CPSs) are becoming more automated and aimed to be as efficient as possible by enabling integration between their operations and Information Technology (IT) resources. In combination with production automation, these systems need to identify their assets and the correlation between them; any potential threats or failures alert the relevant user/department and suggest the appropriate remediation plan. Moreover, identifying critical assets in these systems is essential. With numerous research and technologies available, assessing IT assets nowadays can be straightforward to implement. However, there is one significant issue of evaluating operational technology critical assets since they have different characteristics, and traditional solutions cannot work efficiently. This study presents the necessary background to attain the appropriate approach for monitoring critical assets in CPSs' Situational Awareness (SA). Additionally, the study presents a broad survey supported by an in-depth review of previous works in three important aspects. First, it reviews the applicability of possible techniques, tools and solutions that can be used to collect detailed information from such systems. Secondly, it covers studies that were implemented to evaluate the criticality of assets in CPSs, demonstrates requirements for critical asset identification, explores different risks and failure techniques utilised in these systems and delves into approaches to evaluate such methods in energy systems. Finally, this paper highlights and analyses SA gaps based on existing solutions, provides future directions and discusses open research issues.

Spot pricing is often suggested as a method of increasing demand-side flexibility in electrical power load. However, few works have considered the vulnerability of spot pricing to financial fraud via false data injection (FDI) style attacks. The authors consider attacks which aim to alter the consumer load profile to exploit intraday price dips. The authors examine an anomaly detection protocol for cyber-attacks that seek to leverage spot prices for financial gain. In this way the authors outline a methodology for detecting attacks on industrial load smart meters. The authors first create a feature clustering model of the underlying business, segregated by business type. The authors then use these clusters to create an incentive-weighted anomaly detection protocol for false data attacks against load profiles. This clustering-based methodology incorporates both the load profile and spot pricing considerations for the detection of injected load profiles. To reduce false positives, the authors model incentive-based detection, which includes knowledge of spot prices, into the anomaly tracking, enabling the methodology to account for changes in the load profile which are unlikely to be attacks.

Non-effectively grounded power distribution systems (NGDS) are widely used in China and other countries. However, over a long time, single-phase-to-ground faults (SGF) have been misjudged or omitted by the monitoring system, threatening the security of the power supply system and human safety. Based on the reason analysis for the omitted and misjudged SGFs in NGDS, the concept of NGDS fault eigenparameters to correctly reflect fault characteristics and a method of SGF detection based on fault eigenparameters are proposed. Then, the detection mechanism of phase voltage and fault current resistive elements for SGF is revealed. The variation characteristics of typical fault parameters changing with distribution system scale (parameter) and fault transition resistance, such as residual voltage and Zero-sequence current (iA-iO), are analysed. The eigenparameters of SGF in certain NGDS with specific scales/parameters are also proposed, which can correctly reflect the fault characteristics under different transition resistances.

Although digital technologies on farms bridge the productivity-sustainability gap, they are not yet widely adopted. Familiarising farmers with digital systems is as important as developing advanced technological platforms: As farmers become accustomed to and feel in control of digital systems, they will find it easier to accept, adapt and keep pace with technological developments. This article describes the design and implementation of a flexible, scalable, easy-to-use and extensible IoT embedded system to control sprinkler irrigation in an outdoor Thuja conifer nursery in an automated mode and under varying weather. Irrigation is controlled by Mamdani Fuzzy Inference Logic based on rainfall prediction and real-time monitoring of the plants. The IoT system provides a control dashboard and offers three operating modes: manual, automated or scheduled (daily, weekly, and monthly). The system is robust in the event of a power outage or loss of connectivity. The code is available on GitHub. Sensors, solenoid valves, Raspberry Pi microcontrollers, fuzzy logic systems, a web interface and a cloud service create a sustainable solution that makes water use more efficient, creates a healthy environment for crops (using just the right amount of water), makes life easier for farmers, and exposes them to the benefits of the IoT.

Owing to the availability of sensor data, the operation and maintenance (O&M) of sustainable energy systems have become more intelligent. In particular, data-driven approaches have gained growing interest in supporting intelligent O&M. However, this is not a simple task, as the deficiency of labelled data poses a major challenge. This work proposes a self-supervised pre-training approach for autonomous learning of the Supervisory Control and Data Acquisition (SCADA) data representations for photovoltaic (PV) systems. Specifically, the proposed method first constructs the sample pairs using reasonable assumptions from a large volume of unlabelled SCADA data. Then, it designs a deep Siamese network to extract the representations of the input sample pair and sets the pretext task to measure whether the input pair is similar. The proposed method has been deployed in a PV system with nominal power 2.5 MW located in North China. Experimental results show that the proposed approach achieves accurate similarity assessment for the sample pairs and can potentially support downstream tasks regarding intelligent O&M.

Due to the fast growth of electric vehicles (EVs) , estimation for Battery's State-of-charge (SOC) received significant research interests. The reason is that an accurate SOC estimation can significantly contribute to the reliability of EVs. A Variational Mode Decomposition (VMD) technique enabled Temporal Convolutional Network (TCN) model is proposed by the authors for SOC estimation. The proposed method first adopts time-frequency analysis techniques to decompose voltage values into different frequency domains, each of which is analysed with the VMD technique to obtain its features as the input for the TCN model. Then, the proposed method combines outputs of different frequency domains with an attention module as the final output of the TCN model. Experiments on real battery datasets indicate that the proposed method outperforms the existing methods by 7.2% in mean absolute error and 6.13% in root mean square error. In addition, the error between the estimated and actual values using the proposed method is bounded by 2%.

Models and control techniques for networked control systems (NCSs) can be divided into continuous-time and discrete-time types. Unlike the continuous-time analysis, literature on discrete-time analysis and control of NCSs under packet delay and dropout shows a variety of different models. However, a systematic study of these models is absent in the literature. This article is a methodology for these models. Different factors involved in making this variety are discussed. The models are described and it is shown how they are different or related to each other. The models are from the existing literature. However, to complete the methodology some of the models are introduced by the authors. Furthermore, the concept of sequence matrix is introduced which helps to differentiate some models and should be considered when NCS is analysed as a switched linear system. This methodology can be used as a basis for selecting the suitable model in analysis and design of NCSs. Denial of service (DoS) attack and time delay switch (TDS) attack can be considered as packet dropout and packet delay respectively. Thus, this methodology can also be used in analysis and design of NCS under these cyber-physical attacks.

As a superior modulation strategy, space vector pulse width modulation (SVPWM) provides redundant voltage vectors and adjustable action time, which can achieve multi-objective control of modular multilevel converter (MMC). An SVPWM strategy suitable for MMC is proposed. The strategy is divided into three stages. In the first stage, the appropriate voltage vector, the action time and the basic sub-module (SM) input number are quickly calculated to ensure the output quality by equating MMC as a 2-level inverter. In the second stage, a finite set of the circulating current suppression is established on the basis of the basic SM input number. The optimal SM input number is selected through rolling optimisation. In the last stage, according to the SM voltage sorting and the optimal SM input number, the optimal switching state is determined to realise the SM voltage balance control. The proposed control strategy simplifies the design of the control system, reduces the computational burden and can be easily extended to MMC with any SM number. The simulation and experimental results show that the proposed SVPWM strategy can reduce the circulating current and balance the SM capacitor voltage while ensuring the output quality.

Highly connected smart power systems are subject to increasing vulnerabilities and adversarial threats. Defenders need to proactively identify and defend new high-risk access paths of cyber intruders that target grid resilience. However, cyber-physical risk analysis and defense in power systems often requires making assumptions on adversary behaviour, and these assumptions can be wrong. Thus, this work examines the problem of inferring adversary behaviour in power systems to improve risk-based defense and detection. To achieve this, a Bayesian approach for inference of the Cyber-Adversarial Power System (Bayes-CAPS) is proposed that uses Bayesian networks (BNs) to define and solve the inference problem of adversarial movement in the grid infrastructure towards targets of physical impact. Specifically, BNs are used to compute conditional probabilities to queries, such as the probability of observing an event given a set of alerts. Bayes-CAPS builds initial Bayesian attack graphs for realistic power system cyber-physical models. These models are adaptable using collected data from the system under study. Then, Bayes-CAPS computes the posterior probabilities of the occurrence of a security breach event in power systems. Experiments are conducted that evaluate algorithms based on time complexity, accuracy and impact of evidence for different scales and densities of network. The performance is evaluated and compared for five realistic cyber-physical power system models of increasing size and complexities ranging from 8 to 300 substations based on computation and accuracy impacts.