Generally, wind speed alters continuously during a day and lead to fluctuating output power of a wind farm. Connecting such a wind farm to the transmission line of grid causes a malfunction in the performance of the backup zone of the distance relays due to the random nature of generation and the number of connected wind turbines (wind farm penetration). In this study, a new adaptive setting groups-based method has been proposed to set the second zone of the distance relay in the grid with a wind farm equipped with a doubly fed induction generator (DFIG). In this method, we also taken into account the uncertainty of wind fluctuations and the availability of wind turbines. A Markov model is developed to implement these features. To determine the impedance setting of each of the set groups of the second zone in the distance relays of the remote bus, the obtained impedances are clustered in four clusters by using the combination of K-means and particle swarm optimization (PSO) algorithms. Finally, details of the analysis results of this issue have been compared with other methods available in the literature. The validity and accuracy of the proposed approach also have been assayed and confirmed.

The social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situation. However, most of the data-driven models currently used assume that the input data is complete, which has obvious limitations in real-world applications. This paper suggests an IVS-GAN model to assess power system stability using incomplete PMU measurement data with random loss. The proposed method combines the super-resolution perception technology based on Generative Adversarial Network (GAN) with a time-series signal classification model. The generator adopts a one-dimensional U-Net network and uses convolutional layers to complete and recover missing data. The discriminator adopts a new GRU-Attention architecture proposed in this paper to better extract voltage temporal variation features on key buses. The result of this paper is that the stability evaluation method outperforms other algorithms in high voltage data loss rates on the New England 10-machine 39-bus system.

This study applied the World Health Organization (WHO) guidelines to redesign the Portable Health Clinic (PHC), as a Remote Healthcare System (RHS), for the spread of COVID-19 containment. Additionally, the proposed system not only collects people data but also classifies the case according to the the main symptoms of coronavirus using the COVID-19 triage process (CT-process) based on the analysis of measurement readings taken from patients, where drones are used as a PHC platform and are equipped with the required sensors and essential COVID-19 medications for testing and treating people at their doorstep autonomously when a full curfew is imposed.

In this study, a ZnCo2O4/Ag3PO4 composite catalyst was prepared by the precipitation method, and its photocatalytic degradation activity for methyl orange (MO) was studied. The catalysts were characterized by TEM, XRD, EDX, HRTEM, SAED, SEM, XPS, and UV-Vis-DRS. The results indicate that 0.1 ZnCo2O4/Ag3PO4 The composite system has a good photocatalytic degradation effect on methyl orange. Under 30-minute simulated sunlight conditions, the degradation rate can reach 94%. The results show that the maximum reaction rate constant of 0.1 ZnCo2O4/Ag3PO4 is 0.05301 min-1, which is three times the size of pure Ag3PO4 and 52 times the size of pure ZnCo2O4. After three cycles, 0.1 ZnCo2O4/Ag3PO4 still degraded methyl orange (MO) at a rate of 84.4%. The trapping experiment showed that hole (h+) and O2- played the most important roles in the photocatalytic degradation field of methyl orange (MO) by 0.1 ZnCo2O4/Ag3PO4, and hydroxyl radical (OH·) played a partial role. The energy level structure of ZnCo2O4/Ag3PO4 is conducive to the effective separation of photogenerated electrons and holes, improving the lifespan of photogenerated charges. Among them, the photocatalytic performance of 0.1 ZnCo2O4/Ag3PO4 is the most excellent.

The improvement of the performance of a distributed Bragg reflector laser bar emitting near 905 nm through the use of multiple epitaxially stacked active regions and tunnel junctions is reported. The bar consisting of 48 emitters (each having an aperture of 50 μm) emits an optical power of 2.2 kW in 8 ns long pulses at an injection current of 1.1 kA. This corresponds to an almost threefold increase of the pulse power compared to a bar with lasers having only a single active region. Due to the integrated surface Bragg grating, the bar exhibits a narrow spectral bandwidth of about 0.3 nm and a thermal tuning of only 68 pm/K.

The voltage endurance coefficient n of cross-linked polyethylene (XLPE) insulation is an important indicator for the analysis of insulation failure mechanism. Accurately obtaining the value of n can provide technical support for the reliability evaluation of cable operation. The constant stress method and the step stress method are used to obtain the values of n, but the test parameters can influence the accuracy of the obtained n values. Two methods for selecting test parameters are proposed. The equivalence between the two methods is established, providing theoretical support for the accurate evaluation of the n value of solid insulation materials by using the step stress method. The accuracy of the evaluation results is verified by analyzing the test data. The test results show that the nstep=13.04 evaluated by the step stress test with the proposed parameter selection method is consistent with the test result of constant stress nconstant=12.83.

This paper proposes a background calibration scheme for the pipelined-SAR ADC based on the neural network. Due to the nonlinear function fitting capability of the neural network, the linearity of the ADC is improved effectively. However, the hardware complexity of the neural network limits its application and promotion in ADC calibration. Hence, this paper also presents the optimization schemes, including the neuron-based sharing neural network and the partially binarized with fixed neural network, in terms of calibration architecture and algorithm. A 60 MS/s 14-bit pipelined-SAR ADC prototyped in 28-nm technology is utilized to verify the feasibility of the proposed calibration method. The measurement results show that the proposed calibration enhances the SFDR and SNDR from 68.3 dB and 44.6 dB to 95.4 dB and 65.4 dB at low frequency, and from 56.8 dB and 35.6 dB to 90.6 dB and 63.6 dB at Nyquist frequency. Meanwhile, the original calibrator and improved calibrator are synthesized in Synopsys Design Compiler to compare their hardware complexity. Compared with the unoptimized version, the optimized schemes can decrease the logic area and the network weights up to 76% and 52%, with negligible loss in calibration performance.

The decarbonization of the power generation and transport sector encourage the analysis of connection of , such as , to the electrical system, as well as the evaluation of their impact on smart cities. A better understanding of the negative impacts on the power systems will lead to propose mitigation measures and eventually revolutionize the way distributed generation works. This paper aims at modeling and evaluating the impact of EVs on a real distribution network. The energy system chosen operates at 60 Hz, 34,5 kV (Medium Voltage) and 0,208 kV (Low Voltage) and it is simulated using PSCAD/EMTDC. To reproduce realistic user consumption profiles, dynamic load profiles based on EV owners behaviour have been simulated. The technology is modeled to mitigate the impacts of high penetration of EVs by supporting the network from undervoltage. The results show the importance of active management in modern power systems, especially considering the increase in DER penetration expected for the coming years. This work shows the benefits of implementing V2G technology while highlighting the challenges involved in a real case.

This paper shows the comparison between several well-known classification algorithms in Machine Learning with the purpose to find the most suitable algorithm to predict the dwelling time i.e., how long a certain tourist should stay in a particular tourist spot. This dwelling time prediction can be adopted for tour and travel agents to provide optimal scheduling for their package tour. The algorithm in question is strictly for classification because in this case, the dwelling time does not require a very specific number of minutes, thus the time can be classified and restricted into several time frames. The origin and features of the dataset are described in this paper as well as the comparison methodology to show the procedure of how the comparison was made. Lastly, the performance results will be used to determine which algorithm to use for this specific case and it will be shown in a form of a graph

Recently, power interruptions with high impact effects are increased by occurring natural disasters. Furthermore, the uncertain nature of data creates the considerable challenges for enhancing the resiliency of power distribution systems after occurring events. Regarding this, this paper presents a robust optimization approach for resiliency improvement in power distribution system. The robust approach uses the crew teams for switching action as restoration process, demand response programs and Mobile Generators (MGs) simultaneously for improving the resiliency with considering the uncertainty of electrical load and electrical price. The objective function is tri-level consists of minimum, maximum and minimum function. The first level is minimum function that is for minimizing cost of commitment of CHPs with considering location of MGs and reconfiguration structure in power distribution systems. The second level is maximum function that is for finding the worst-case scenario of the uncertainty variables. The third level is minimizing total operation cost in the condition with worst scenario of stochastic data by using demand response programs. The aforementioned algorithm is implemented on an IEEE 33-bus test distribution system with four different cases. Furthermore, several cases and sensitivity analysis are accomplished in order to show the efficiency of the proposed model.

There can be numerous electronic components on a given PCB, making the task of visual inspection to detect defects very time-consuming and prone to error, especially at scale. There has thus been significant interest in automatic PCB component detection, particularly leveraging deep learning. However, deep neural networks typically require high computational resources, possibly limiting their feasibility in real-world use cases in manufacturing, which often involve high-volume and high-throughput detection with constrained edge computing resource availability. As a result of an exploration of efficient deep neural network architectures for this use case, we introduce PCBDet, an attention condenser network design that provides state-of-the-art inference throughput while achieving superior PCB component detection performance compared to other state-of-the-art efficient architecture designs. Experimental results show that PCBDet can achieve up to 2× inference speed-up on an ARM Cortex A72 processor when compared to an EfficientNet-based design while achieving ∼2-4% higher mAP on the FICS-PCB benchmark dataset.

A method for identifying vulnerable nodes in distribution networks is proposed, which is based on complex networks and optimized TOPSIS. This method aims to address the issues of one-sided evaluation indicators and inaccurate indicator weights that are present in existing methods for identifying vulnerable nodes in distribution networks. Based on the theory of complex networks, a comprehensive set of vulnerability indicators for distribution network nodes is constructed by considering both the topology structure and system operation status of the distribution network. The TOPSIS comprehensive evaluation model for optimization is proposed to enhance the selection process of optimal and worst indicator values. The advantages and disadvantages of each indicator are characterized using Mahalanobis distance. The calculation of proximity is optimized by establishing a virtual negative ideal solution, which makes the identification of vulnerable nodes more reasonable. The simulation results demonstrate that this method is more effective in identifying vulnerable nodes in the power grid compared to traditional methods, and has significant practical applications.

5G network slicing is a promising solution to prioritize time-critical protection communication in wireless networks. However, recent trends indicate that a 5G slice could encompass all smart grid applications lacking the necessary granularity. At the same time, while substation communication standards recommend prioritization of protection communication traffic to improve reliability, these recommendations are only for wired connections. Therefore, this paper investigates traffic shaping and uplink (UL) bitrate adaptation of video stream based on existing commercial solutions as methodologies for prioritizing the protection communication in a 5G slice. These methodologies are validated in an experimental setup combining controller-hardware-in-the-loop (CHIL) simulation with a quality of service (QoS) measurement system. The system under test consists of commercial 5G networks, commercial intelligent electronic devices (IEDs), and merging units to validate the methodologies on three smart grid applications: fault location, line differential, and intertrip protection. The results show improvement in protection communication when traffic shaping and UL bitrate adaptation are applied. Traffic shaping even improves prioritization with a wired connection.

Pavement distress classification is a vital step for automatic pavement inspection and maintenance. Recently, patch-based approaches have achieved promising performances and thus extensive attention in this field. However, these methods simply assume that all patches contribute equally to the distress classification, leading to weakly discriminating abilities of models. Moreover, their tedious processes also leads to a low efficiency in inference. In this letter, we present a novel patch-based pavement distress classification approach named Deep Patch Soft Selective Learning (DPS$^2$L), which addresses these issues. Similar to other patch-based approaches, DPS$^2$L partitions the pavement images into patches and aggregates the patch features to accomplish the task. To address the first issue, we introduce a succinct Soft Patch Feature Selection Network (SPFSN) to assess the importance of each patch to the distress classification with a score based on its feature. These scores will be considered as patch-wise weights for feature aggregation. In such a manner, the most discriminative patches are selected in a soft way, and thereby benefit the final classification. To address the inference efficiency issue, knowledge distillation is leveraged to transfer the classification knowledge from DPS$^2$L to the image-based approaches, such as EfficientNet-B3. This distilled model enables incorporating both the advantages of patch-based approaches in classification performance and the advantages of image-based approaches in inference efficiency. Extensive experiments on a large-scale pavement image dataset named CQU-BPDD demonstrates the superiority of our methods over baselines regardless of performance or efficiency.

Terahertz (THz) imaging has an outstanding advantage of high resolution due to the high frequency and has promising potential in VideoSAR. However, limited to the THz source power and the air absorption, the THz image usually has a low SNR and is susceptible to noise in remote sensing and imaging. In order to improve the quality of THz images, a THz image enhancement method is proposed based on the noise2noise idea. The THz images are reconstructed with the AFBP algorithm. They are organized as noisy image pairs and filtered with a mask to remove the influence of moving targets. Then, the Noise2Noise network is constructed based on the CNN network and takes the noisy image pair as input and reference. In the training stage, 1000 noisy image pairs are used as the training set and 100 noisy images are used as the test set to verify the performance of the proposed method. The experimental results based on real VideoSAR data demonstrate that the proposed method is capable of suppressing noise and enhancing the THz image.

Rydberg-atom electrometers have the remarkable advantages of self-calibration and high sensitivity. Based on the classical electromagnetic theory, a localized electric field enhancement structure of a hybrid rectangular resonator is proposed to improve the sensitivity of quantum microwave measurement. It should be noted that the prototype of the hybrid rectangular resonator is fabricated and measured at 9.925 GHz. The results of full-wave simulations show that the uniform and high electric field enhancement in the TE101 fundamental mode is realized. The transient process of resonance is simultaneously simulated, and the time to settle steady state is given as about 104 ns. As indicated through experimental results that the structure can reach 24 dB (enhancement factor of 15.8). As a result, the method proposed in this study, based on atomic measurement capabilities, enables us to improve the measurement sensitivity further and promotes the practical development of quantum microwave measurement technology.

This paper proposes a recurrent neural network (RNN) based model to segment and classify multiple combined multiple power quality disturbances (PQDs) from the PQD voltage signal. A modified bi-directional long short-term memory (BI-LSTM) model with two different types of attention mechanism is developed. Firstly, an attention gate is added to the basic LSTM cell to reduce the training time and focus the memory on important PQD signal part. Secondly, attention layer is added to the BI-LSTM to obtain the more important part of the voltage signal by assigning weightage to the output of the BI-LSTM model. Finally a SoftMax classifier is applied to classify the combined PQD signal in 96 different combinations. The performance of proposed BI-LSTM model with attention gate and attention layer mechanism is compared with the performance of baseline models based on recurrent neural network (RNN) and convolution neural network (CNN). With this model, the PQD signal is easily segmented from the voltage signal which makes the process of PQD classification more accurate with less computation complexity and in less time.

The health monitoring for disease diagnosis and prognosis in a desired smart medical structure is realized by interpreting the health data. The advances in sensor technologies and biomedical data acquisition tools have led to the new era of big data, where different sensors collect massive medical data every day. This special issue explores the latest development in emerging technologies of biomedical engineering, including big medical data, artificial intelligence, cloud/fog computing, federated learning, ubiquitous computing and communication, internet of things, wireless technologies, and, security and privacy. The biological wearable sensors can enhance the decision-making and early disease diagnosis processes by intelligently investigating and collecting large amounts of biomedical data (i.e., big health data). Hence, there is a need for scalable advanced learning, and intelligent algorithms that lead to reliable and interoperable solutions to make effective decisions in emergency medicine technologies. The optimization algorithms can be used in order to acquire the sensor data from multiple sources for fast and accurate health monitoring.