Search Results (1,793)

This paper proposes an intelligent power adaptation framework for Free-Space Optics (FSO) communication systems operating under different weather conditions exploiting a deep learning (DL) analysis of received eye diagram images. The system incorporates two Convolutional Neural Network (CNN) architectures, LeNet and Wide Residual Network (Wide ResNet) algorithms to perform regression tasks that predict received signal quality metrics such as the Quality Factor (Q-factor) and Bit Error Rate (BER) from the received eye diagram. These models are evaluated using Mean Squared Error (MSE) and the coefficient of determination (${\mathrm{R}}^2$ score) to assess prediction accuracy. Additionally, a custom CNN-based classifier is trained to determine whether the BER reading from the eye diagram exceeds a critical threshold of ${10}^{-4}$; this classifier achieves an overall accuracy of 99%, correctly detecting 194/195 “acceptable” and 4/5 “unacceptable” instances. Based on the predicted signal quality, the framework activates a dual-amplifier configuration comprising a pre-channel amplifier with a maximum gain of 25 dB and a post-channel amplifier with a maximum gain of 10 dB. The total gain of the amplifiers is adjusted to support the operation of the FSO system under all-weather conditions. The FSO system uses a 15 dBm laser source at 1550 nm. The DL models are tested on both internal and external datasets to validate their generalization capability. The results show that the regression models achieve strong predictive performance, and the classifier reliably detects degraded signal conditions, enabling the real-time gain control of the amplifiers to achieve the quality of transmission. The proposed solution supports robust FSO communication under challenging atmospheric conditions including dry snow, making it suitable for deployment in regions like Northern Europe, Canada, and Northern Japan. Full article

The penetration rate of variable energy sources in the current power grid is increasing, with the aim being to expand the use of these energy sources and to replace the traditional black start power supply. This study investigates the black start of a photovoltaic storage joint system based on the system’s short-circuit ratio threshold. Firstly, the principles and control modes of the photovoltaic (PV) system, energy storage system (ESS), and high-voltage direct current (DC) transmission system are studied separately to build an overall model; secondly, computational determinations of the short-circuit ratio under different scenarios are introduced to analyze the strength of the system, and the virtual inertia and virtual damping of the PV system are configured based on this; finally, the change trend of the storage system’s state of charge (SOC) is computed and observed, and the limits of what the system can support in each stage are determined. An electromagnetic transient simulation model of a black start system is constructed in PSCAD/EMTDC, and according to the proposed recovery strategy, the system frequency is maintained in the range of 49.4~50.6 Hz during the entire black start process; the fluctuation in maximum frequency after the recovery of the DC transmission system is no more than 0.1%; and the fluctuation in photovoltaic power at each stage is less than 3%. In addition, all the key indexes meet the requirements for black start technology, which verifies the validity of the strategy and provides theoretical support and a practical reference for the black start of a grid with variable energy sources. Full article

This paper presents the design, implementation, and experimental validation of a Condition Monitoring (CM) circuit for SiC-based Power Electronics Converters (PECs). The paper leverages in situ drain–source resistance (R_ds,on) measurements, interfaced with cloud connectivity for data processing and lifetime assessment, addressing key limitations in current state-of-the-art (SOTA) methods. Traditional approaches rely on expensive data acquisition systems under controlled laboratory conditions, making them unsuitable for real-world applications due to component variability, time delay, and noise sensitivity. Furthermore, these methods lack cloud interfacing for real-time data analysis and fail to provide comprehensive reliability metrics such as Remaining Useful Life (RUL). Additionally, the proposed CM method benefits from noise mitigation during switching transitions by utilizing delay circuits to ensure stable and accurate data capture. Moreover, collected data are transmitted to the cloud for long-term health assessment and damage evaluation. In this paper, experimental validation follows a structured design involving signal acquisition, filtering, cloud transmission, and temperature and thermal degradation tracking. Experimental testing has been conducted at different temperatures and operating conditions, considering coolant temperature variations (40 °C to 80 °C), and an output power of 7 kW. Results have demonstrated a clear correlation between temperature rise and R_ds,on variations, validating the ability of the proposed method to predict device degradation. Finally, by leveraging cloud computing, this work provides a practical solution for real-world Wide Band Gap (WBG)-based PEC reliability and lifetime assessment. Full article

Space-Air-Ground Integrated Network (SAGIN), which is considered a network architecture with great development potential, exhibits significant cross-domain collaboration characteristics at present. However, most of the existing works ignore the matching and adaptability of differential tasks and heterogeneous resources, resulting in significantly inefficient task execution and undesirable network performance. As a consequence, we formulate a category attribute-oriented resource allocation and task offloading optimization problem with the aim of minimizing the overall scheduling cost. We first introduce a task–resource matching matrix to facilitate optimal task offloading policies with computation resources. In addition, virtual queues are constructed to take the impacts of randomized task arrival into account. To solve the optimization objective which jointly considers bandwidth allocation, transmission power control and task offloading decision effectively, we proposed a deep reinforcement learning (DRL) algorithm framework considering type matching. Simulation experiments demonstrate the effectiveness of our proposed algorithm as well as superior performance compared to others. Full article

Pain following orthodontic treatment is the chief complaint of patients undergoing this form of treatment. Although the use of diode lasers has been suggested for pain reduction, the mechanism of laser-induced analgesic effects remains unclear. Neuropeptides, such as substance P (SP) and calcitonin gene-related peptide (CGRP), contribute to the transmission and maintenance of inflammatory pain. Heat shock protein (HSP) 70 plays a protective role against various stresses, including orthodontic forces. This study aimed to examine the effects of diode laser irradiation on neuropeptides and HSP 70 expression in periodontal tissues induced by experimental tooth movement (ETM). For inducing ETM for 24 h, 50 g of orthodontic force was applied using a nickel–titanium closed-coil spring to the upper left first molar and the incisors of 20 male Sprague Dawley rats (7 weeks old). The right side without ETM treatment was considered the untreated control group. In 10 rats, diode laser irradiation was performed on the buccal and palatal sides of the first molar for 90 s with a total energy of 100.8 J/cm². A near-infrared (NIR) laser with a 808 nm wavelength, 7 W peak power, 560 W average power, and 20 ms pulse width was used for the experiment. We measured the number of facial groomings and vacuous chewing movements (VCMs) in the ETM and ETM + laser groups. Immunohistochemical staining of the periodontal tissue with SP, CGRP, and HSP 70 was performed. The number of facial grooming and VCM periods significantly decreased in the ETM + laser group compared to the ETM group. Moreover, the ETM + laser group demonstrated significant suppression of SP, CGRP, and HSP 70 expression. These results suggest that the diode laser demonstrated analgesic effects on ETM-induced pain by inhibiting SP and CGRP expression, and decreased HSP 70 expression shows alleviation of cell damage. Thus, although further validation is warranted for human applications, an NIR diode laser can be used for reducing pain and neuropeptide markers during orthodontic tooth movement. Full article

Transmission towers are particularly vulnerable to extreme wind events, which can lead to structural damage or collapse, thereby compromising the stability of power transmission systems. Enhancing the wind-resistant capacity of these towers is therefore critical for improving the reliability and resilience of electrical infrastructure. This study utilizes finite element analysis (FEA) to evaluate the structural response of a 220 kV transmission tower subjected to fluctuating wind loads, effectively capturing the dynamic characteristics of wind-induced forces. A comprehensive dynamic analysis is conducted to account for uncertainties in wind loading and variations in wind direction. Through this approach, this study identifies the most critical wind angle and local structural weaknesses, as well as determines the threshold wind speed that precipitates structural collapse. To improve structural resilience, a concurrent multi-scale modeling strategy is adopted. This allows for localized analysis of vulnerable components while maintaining a holistic understanding of the tower’s global behavior. To mitigate failure risks, the traditional perforated plate reinforcement technique is implemented. The reinforcement’s effectiveness is evaluated based on its impact on load-bearing capacity, displacement control, and stress redistribution. Results reveal that the critical wind direction is 45°, with failure predominantly initiating from instability in the third section of the tower leg. Post-reinforcement analysis demonstrates a marked improvement in structural performance, evidenced by a significant reduction in top displacement and stress intensity in the critical leg section. Overall, these findings contribute to a deeper understanding of the wind-induced fragility of transmission towers and offer practical reinforcement strategies that can be applied to enhance their structural integrity under extreme wind conditions. Full article

In some areas, such as Gansu in China and Texas in the USA, lots of wind power bases are located far away from load centers. Transmitting large amounts of wind power to load centers through long transmission lines will lead to wind turbines being integrated into a weak grid, which decreases the stability limits of wind turbines. To solve this problem, this study investigates the stability limits of a Doubly Fed Induction Generator (DFIG) connected to a weak grid in a DC-link voltage control timescale. To start with, a model of the DFIG in a DC-link voltage control timescale is presented for stability limit analysis, which facilitates profound physical understanding. Through steady-state stability analysis based on sensitivity evaluation, it is found that the critical factor restricting the stability limit of the DFIG connected to a weak grid is ∂P_e/∂ (−i_rd), changing from positive to negative. As ∂P_e/∂ (−i_rd) reaches zero, the system reaches its stability limit. Furthermore, by considering control loop dynamics and grid strength, the stability limit of the DFIG is investigated based on eigenvalue analysis with multiple physical scenarios. The results of root locus analysis show that, when the DFIG is connected to an extremely weak grid, reducing the bandwidth of the PLL or increasing the bandwidth of the AVC with equal damping can increase the stability limit. The aforesaid theoretical analysis is verified through both time domain simulation and physical experiments. Full article

This review examines the design of thermal control systems for state-of-the-art deep ultraviolet (DUV) and extreme ultraviolet (EUV) projection lithography tools. The lithographic system under investigation integrates several critical subsystems along the optical transmission chain, including the light source, reticle stage, projection optics (featuring DUV refractive lenses and EUV multilayer mirrors), immersion liquid, wafer stage, and metrology systems. Under high-power irradiation conditions with concurrent thermal perturbations, the degradation of thermal stability and gradient uniformity within these subsystems significantly compromises exposure precision. Through a systematic analysis of the thermal challenges specific to each subsystem, this review synthesizes established thermal control systems across two technical dimensions: thermal control structures and thermal control algorithms. Prospects for future advancements in lithographic thermal control are also discussed. Full article

Under the “dual carbon” goals and rapid clean energy development, power grids face challenges including rapid load growth, uneven power flow distribution, and limited transmission capacity. This paper proposes a novel FACTS device with fault tolerance and switchable topology that maintains power flow control over multiple lines during N-1 faults, enhancing grid safety and economy. The paper establishes a steady-state mathematical model based on additional virtual nodes and provides power flow calculation methods to accurately reflect the device’s control characteristics. An entropy-weighted TOPSIS method was employed to establish a quantitative evaluation system for assessing the grid performance improvement after FACTS device integration. To address interaction issues among multiple flexible devices, an optimization planning model considering th3e coordinated effects of UPFC and VSC-HVDC was constructed. Multi-objective particle swarm optimization obtained Pareto solution sets, combined with the evaluation system, to determine the optimal configuration schemes. Considering wind power uncertainty and fault risks, we propose a system-level coordinated operation strategy. This strategy constructs probabilistic risk indicators and introduces topology switching control constraints. Using particle swarm optimization, it achieves a balance between safety and economic objectives. Simulation results in the Jiangsu power grid scenarios demonstrated significant advantages in enhancing the transmission capacity, optimizing the power flow distribution, and ensuring system security. Full article

Non-orthogonal multiple access (NOMA) technology can effectively improve spectrum efficiency, unmanned aerial vehicle (UAV) communication has the advantage of flexible deployment, and reconfigurable intelligent surface (RIS) can intelligently control the wireless transmission environment. Traditional communication systems have problems such as limited coverage and low spectrum efficiency in complex scenarios. However, a key challenge in deploying RIS-assisted NOMA-UAV communication systems lies in how to jointly optimize the UAV flight trajectory, power allocation strategy, and RIS phase offset to achieve the maximum average achievable rate for users. The non-convex nature of the optimization complicates the problem, making it challenging to find an efficient solution. Based on this, this paper presents a RIS-assisted NOMA-UAV communication system consisting of one UAV, one RIS, and two ground users. To achieve the maximum average rate for users, the UAV flight trajectory, power allocation strategy, and RIS phase offset are jointly optimized. For the non-convex problem, we decompose it into three sub-problems based on its inherent structural characteristics and use an alternating iterative approach to gradually converge to a feasible solution. The simulation results demonstrate that the proposed scheme offers significant advantages in the application scenario. Compared to other benchmark schemes, it delivers superior performance improvements to the communication system and offers higher practical value. Full article

Communication delay, as a key factor restricting the rapid and accurate transmission of data in the smart grid, will affect the collaborative operation of power electronic devices represented by the Distributed Power Flow Controller (DPFC), and further affect the construction and safe and stable operation of the new power system. Aiming at the problem of DPFC communication delay prediction, this paper proposes a new SAR-ARIMA-LSTM hybrid prediction model. This model introduces the spatial autoregressive model (SAR) on the basis of the traditional ARIMA-LSTM model to extract the spatial correlation between devices caused by geographical location and communication load, and then combines ARIMA-LSTM prediction. The experimental structure shows that compared with the traditional ARIMA-LSTM model, the model proposed in this paper predicts that RMSE decreases from 1.59 to 1.2791 and MAE decreases from 1.27 to 1.0811, with a reduction of more than 14%. The method proposed in this paper can effectively reduce the communication delay prediction data of DPFC at different spatial positions, has a stronger ability to handle high-delay fluctuations, and provides a new technical approach for improving the reliability of the power grid communication network. Full article

Renewable energy sources (RES) are the foundation of the ongoing energy transition in Poland and worldwide. However, increased use of RES has brought several challenges, as most of these sources are dependent on weather conditions. The instability and lack of control over electricity production lead to both overloads and power shortages in transmission and distribution networks. A significant advantage of biogas plants over sources such as photovoltaics or wind turbines is their ability to control electricity generation and align it with actual demand. Biogas produced during fermentation can be temporarily stored in a biogas tank above the digester and later used in an enlarged CHP unit to generate electricity and heat during peak demand periods. While demand-driven biogas plants operate similarly to traditional installations, their development requires navigating regulatory and administrative procedures, particularly those related to the grid connection of the generated electricity. In Poland, it has only recently become possible to obtain grid connection conditions for such installations, following the adoption of the Act of 28 July 2023, which amended the Energy Law and certain other acts. However, the biogas sector still faces challenges, particularly the need for effective incentive mechanisms and the removal of regulatory and economic barriers, especially given its estimated potential of up to 7.4 GW. Full article

Bipolar power supply can effectively reduce line losses and optimize power transmission. This paper proposes a low-power bipolar DC–DC converter with voltage self-balancing, which not only achieves bipolar output but also automatically balances the inter-pole voltage under load imbalance conditions without requiring additional voltage balancing control. This paper first elaborates on the derivation process of the proposed converter, then analyzes its working principles and performance characteristics. A 400 W experimental prototype is built to validate the correctness of the theoretical analysis and the voltage self-balancing capability. Finally, loss analysis and conclusions are presented. Full article

Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. Full article

Cross-validation and open research on future electric grids, particularly in their stability modeling and dynamic performance, can greatly benefit from high-fidelity, publicly available test cases, since access to dynamic response models of actual grid models is often limited due to legitimate security concerns. This paper presents a methodology for designing and validating the dynamic performance of large, IBR-heavy synthetic grids, that is, realistic but fictitious test cases. The methodology offers a comprehensive framework for creating dynamic models for both synchronous generators (SGs) and inverter-based resources (IBRs), focusing on realism, controllability, and flexibility. For realistic dynamic performance, the parameters in each dynamic model are sampled based on statistical data from benchmark actual grids, considering power system dynamics such as frequency and voltage control, as well as oscillation response. The paper introduces system-wide governor design, which improves the controllability of parameters in dynamic models, resulting in a more realistic frequency response. As an example, multiple case studies on a 2000-bus Texas synthetic grid are shown; these represent realistic dynamic performance under different transmission conditions in terms of frequency, voltage control, and oscillation response. Full article