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

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.

With the rapid development of biomedical research and information technology, the number of clinical medical literature has increased exponentially. At present, COVID-19 clinical text research has some problems, such as lack of corpus and poor annotation quality. In clinical medical literature, there are many medical related semantic relationships between entities. After the task of entity recognition, how to further extract the relationships between entities efficiently and accurately becomes very critical. In this study, a COVID-19 clinical trial data relationship extraction model based on deep learning method is proposed. The model adopts MPNet model, bidirectional-GRU (BiGRU) network, MAtt mechanism and Conditional Random Field inference layer integration architecture and improves the problem that static word vector cannot represent ambiguity through pre-trained language model. BiGRU network is used to replace the current Bi directional long short term memory structure and simplify the network structure of Long Short Term Memory to improve the training efficiency of the model. Through comparative experiments, the proposed method performs well in the COVID-19 clinical text entity relation extraction task.

In order to deal with the problems of complex optimisation model and computing speed in the multi-objective operation control of the power distribution network, this paper is oriented to the basic model and process of the feeder partition decoupling method for the distribution network. Firstly, the necessity of developing partition decoupling for the distribution network is expounded according to the development status and control mode of the complex distribution network. Secondly, the objective models commonly used in the distribution network optimisation control are given to illustrate the importance of partition decoupling for the complex distribution network, including line loss and voltage offset. Finally, three general decoupling equivalent models are presented, namely Ward equivalent model, virtual generator equivalent model, and radial equivalent independent model, and then the partition decoupling equivalent process is proposed.

In this study, a partition optimisation method for distribution network is proposed to realise decoupling coordination, which provides a research basis for optimising power flow and operation regulation. Firstly, a partition model of distribution network is established, in which the electrical distance, parallel computing efficiency, and operation stability indexes are considered at the same time; Secondly, the AHC algorithm is used to realise the automatic search partition of the distribution network, and the class spacing measurement factors of point-to-point, cluster-to-cluster are considered in this method. Finally, the Distributed Sequential Quadratic Programming for Distributed Generation (DSQP-DG) is introduced, and the parallel decoupling coordination of the distribution network is realised by alternate iteration of its inner and outer layers.

In order to accurately extract the fault features of arc high impedance grounding of low-voltage distribution lines and judge the fault feature types of arc high impedance grounding of low-voltage distribution lines, a fault feature extraction method for arc high impedance grounding of low-voltage distribution lines based on Bayesian network optimisation algorithm is proposed. According to the model of arc high impedance grounding fault based on Thomson’s principle, the parameter information of each transmission signal in arc high impedance grounding fault is extracted. Through the denoising method of arc high impedance grounding signal based on combined filter, the noise information of transmission signal in case of arc high impedance grounding fault is removed and the signal purity is improved. The detection and recognition method for fault characteristics of arc high impedance grounding of low-voltage distribution lines based on Bayesian network optimisation algorithm is used to detect and judge the fault characteristics of the abnormal characteristics of the denoised transmission signal, and complete the fault feature extraction. After testing, this method can accurately and real-time extract the fault characteristics of arc high impedance grounding of low-voltage distribution lines, and has application value.