Search Results (703)

Photovoltaic (PV) performance in desert environments is significantly hindered by soiling and partial shading. To bridge the gap in empirical data for extreme Saharan conditions, this study presents a novel techno-economic assessment of uniform horizontal shading (UHS) specifically conducted in Mauritania. Controlled outdoor experiments were performed using a 250 W crystalline silicon PV module and a PVPM 2540C I–V curve tracer, applying progressive shading levels from 2.5% to 20%. The novelty of this work lies in the integration of high-resolution experimental I–V/P–V characterization with a localized techno-economic model for five pre-commercial PV plants. It was observed that PV modules are exceptionally sensitive to shading; specifically, a mere 10% shaded area leads to a catastrophic 90% drop in power and current, while the voltage remains remarkably stable. Thermographic analysis further validates the thermal gradients and bypass diode functionality. By quantifying the financial impacts, this research highlights that cumulative economic losses across the five real-world sites reached 87.95%, exceeding 55,000 MRU. These findings provide a strategic framework for optimizing PV systems in arid terrains and offer a robust tool for enhancing the design and operation of large-scale solar applications in desert environments. Full article

Fault-induced delayed voltage recovery (FIDVR) poses a serious threat to modern power grid operation, where stalled induction motors following a fault can sustain dangerously low bus voltages and potentially trigger cascading failures. While deep reinforcement learning (DRL) has shown promise for emergency load shedding control, existing centralized DRL approaches require extensive communication infrastructure and large neural network models that are computationally prohibitive to train at scale. Fully decentralized approaches, on the other hand, lack inter-agent information sharing and coordination, often resulting in inadequate voltage recovery across area boundaries. To address these limitations, we propose a Cloud–Edge Collaborative DRL framework that combines lightweight, area-specific edge agents for local load shedding control with a supervisory cloud agent that coordinates their actions globally, achieving scalable training and system-wide voltage recovery simultaneously. Training is further accelerated through a modified Guided Surrogate-gradient-based Evolutionary Random Search (GSERS) algorithm. Validation on the IEEE 300-bus system demonstrates that the proposed framework reduces training time by approximately 90% compared to the fully centralized approach, while achieving comparable voltage recovery performance to the centralized method and approximately 80% better reward performance than the fully decentralized approach, confirming the critical benefit of the cloud-level coordination mechanism. Full article

Wireless power transfer (WPT) systems have garnered significant market attention owing to their broad applicability in portable electronic devices, electric vehicles, unmanned aerial vehicles, biomedical implants, and related fields. In these systems, operating frequency and efficiency are critical factors affecting both transmission efficiency and transmission distance, making high-frequency operation an important trend for improving overall WPT performance. However, elevating the switching frequency also introduces notable challenges, including increased switching losses in power devices, limited load adaptability, and poor anti-misalignment capability, which in practice often lead to degraded system efficiency and unsatisfactory waveform quality. Accordingly, this paper proposes a high-frequency inverter power supply system capable of operating at a maximum output voltage frequency of 25 KHz. Under conditions of a 10 KHz output frequency and a 20 KΩ load, the system achieves a peak efficiency of 94.01%. A prototype was implemented through the integration of a software algorithm based on ARM Cortex-M3 core control with a hardware architecture consisting of a driving circuit, a full-bridge inverter, and a switchable filtering module. This work offers practical design insights for the development of future high-frequency, high-voltage inverter systems, while also providing valuable experimental data to support further research in this area. Full article

As a next-generation serial communication protocol employed in automotive electronics and industrial control domains, Controller Area Network with Flexible Data-Rate (CAN-FD) enhances communication efficiency via the introduction of a dual-rate transmission mechanism, yet it still inherits the security vulnerabilities of traditional CAN networks. To enhance the security of node identity authentication in CAN-FD networks—a critical prerequisite for secure communication—we present an electronic control unit (ECU) authentication scheme that utilizes voltage hardware fingerprints (VHFs) as the core identity credential. Specifically, a single frame of data is utilized to integrate the control field’s voltage characteristics and data field’s edges, forming stable and distinguishable hardware fingerprints. We also analyze the VHF offset characteristics under typical spoofing attacks and wire-tapping attacks, and then propose a lightweight vehicle intrusion detection system (VIDS) scheme to identify attack scenarios and locate the compromised ECU in CAN-FD networks. Lastly, we conducted research on and discussed other VHF-influencing factors and put forward detailed specific solutions. Attack tests are conducted under four representative scenarios, namely substitution attack, masquerade attack, injection attack, and wire-tapping attack. The findings reveal that our scheme can not only accurately distinguish between various CAN-FD nodes but also identify specific attack types in real time. In detail, a single-frame node recognition rate exceeding 99% is achieved in approximately 2 ms, and in experiments covering multiple attack scenarios on this six-node prototype system, 100% recognition accuracy for attack types is realized in approximately 500 ms. Full article

Capacitive Power Transfer (CPT) technology, as an emerging wireless power supply solution, exhibits great potential in areas such as electric vehicle charging, underwater equipment power supply, biomedical implants, and consumer electronics due to its advantages of low cost, light weight, insensitivity to metals, and potential high power density. However, the coupling capacitance is susceptible to the influence of transmission distance, misalignment, and changes in environmental media, leading to fluctuations in system output characteristics and becoming a key challenge restricting its application. This report aims to systematically review the key technological advancements proposed in recent years to achieve constant voltage/current/power output and enhance system robustness. Firstly, this study categorically reviews the CPT system topologies for constant voltage output, constant current output, and constant power output, analyzing the principles, advantages, and disadvantages of achieving load-independent or coupling-independent output. Secondly, it sorts out various active and passive control strategies, including frequency regulation, impedance matching, adaptive parameter switching, and pulse modulation, which are used to manage dynamic changes. Next, it summarizes innovative design and optimization methods for couplers tailored to specific application scenarios, such as large-gap electric vehicle charging, underwater, and rotating mechanisms. Finally, based on existing research, this review describes the challenges that CPT technology still faces in achieving efficient, high-power, and highly robust constant output, and looks forward to future research directions. Full article

In light of the low efficiency and unstable power transmission capacity of grid-connected energy storage in photovoltaic (PV) systems, this paper proposes a maximum power point tracking (MPPT) control strategy based on a novel intelligent fusion algorithm. We begin by highlighting that improving the power generation efficiency of PV systems under non-ideal conditions—such as partial shading—is a key challenge for increasing the utilization rate of renewable energy and promoting the sustainability of energy systems. The proposed strategy integrates two complementary search algorithms: the Whale Optimization Algorithm (WOA) for global exploration and the Sparrow Search Algorithm (SSA) for local exploitation. These are combined through a weighted superposition mechanism to enhance the overall search balance. First, a Chaotic Map is used to initialize the populations of both WOA and SSA, while population weights are restructured to improve diversity. Subsequently, a weighted superposition mechanism reorganizes the initialized populations to generate a fused WOSSA population, enabling a global search that produces and evolves a set of optimal solutions across the entire search space, further enhancing search diversity. Then, a local search is applied to selected high-quality individuals to prevent premature convergence and rapidly exploit promising regions. Finally, boundary-handling functions and a power restart mechanism are introduced during the population position update phase to refine position updates in the WOSSA algorithm. This optimizes the iterative process, accelerates convergence, and strengthens the algorithm’s ability to escape local optima. The proposed algorithm is simulated in MATLAB. Simulation results demonstrate that, compared with the SSA algorithm, the convergence speed is improved by approximately 55%, the maximum power tracking performance is enhanced by about 70%, and the voltage of the energy storage unit remains above 380 V. Experimental validation further shows that the PV system achieves an average daily output of 425.5 V and 4.3 A, a grid frequency of 49.9 Hz, a daily energy yield of 0.5 kWh, and a power generation efficiency per unit installation area of 2 kW. The method also exhibits good performance in improving the quality of grid-connected power. Full article

With the core advantages of high energy efficiency, high power density, and reliable operation, high-voltage permanent magnet motors have become the mainstream development direction of modern motor technology. However, the risk of demagnetization caused by excessive temperature increases in permanent magnets has become a key bottleneck restricting motor performance and operational reliability, which makes research on the flow and heat transfer characteristics of motor cooling systems of great engineering value. Taking the 710 kW high-voltage permanent magnet motors as the research object, this study established a global flow field mathematical model covering the internal and external air duct cooling systems of the motor based on the theories of computational fluid dynamics and numerical heat transfer, and systematically analyzed the flow characteristics and distribution laws of cooling air. The thermal–fluid coupling numerical method was employed to simulate the temperature field of the motor, and the overall temperature distribution of the motor, temperature gradient of key components, and maximum temperature value were accurately obtained. To verify the validity of the established model, a test platform for the cooling system performance was designed and built. Measuring points for wind speed, air temperature, and component temperature were arranged at key positions, such as the stator radial ventilation ducts, and experimental tests were conducted under the rated operating conditions. The results show that the flow field distribution of the internal and external air ducts of the motor is reasonable and that the cooling air flows uniformly, with the external and internal circulating air volumes reaching 1.2 m³/s and 0.6 m³/s, respectively, which meets the heat dissipation requirements. The maximum temperature of 95 °C occurs in the stator winding area, and the maximum temperature of the permanent magnets is controlled within the safe range of 65 °C. The simulation results were in good agreement with the experimental data, with an average relative error of only 4%, which fell within the engineering allowable range, thus verifying the accuracy and reliability of the established global model and thermal–fluid coupling calculation method. This study reveals the thermal–fluid coupling transfer mechanism of high-voltage permanent magnet motors and provides a theoretical basis and engineering reference for the optimal design, precise temperature rise control, and reliability improvement of motor cooling systems. Full article

Low-voltage instrumentation and control (I&C) cables in nuclear power plants are continuously exposed to gamma (γ) radiation within containment areas, leading to cumulative degradation of their polymer insulation over decades of operation. Since conventional mechanical aging assessments are destructive, this study establishes a non-destructive diagnostic framework using high-frequency dielectric spectroscopy. Cable samples with ethylene propylene rubber (EPR) insulation and chlorosulfonated polyethylene (CSPE) jackets were subjected to controlled γ-irradiation at doses up to 1200 kGy. The broadband dielectric response was analyzed along with derived novel diagnostic parameters from capacitance and loss tangent spectra and a machine learning AI algorithm. The results show a strong, material-dependent relationship between radiation dose and dielectric indicators. For EPR insulation, the central capacitance (CC) and (C × F × LF) exhibit high positive sensitivity for Black and White EPR materials, respectively, whereas for CSPE jackets, the central frequency (CF) shows a pronounced monotonic decrease with the radiation exposure. These findings enable a straightforward, transparent interpretation of dielectric data and implement a new, accurate method of irradiated cables diagnosis. Full article

With more renewables being integrated into distribution grids, the problem of voltage fluctuation has become prominent. Effective Volt/VAR regulation is a commonly used method to ensure the safe operation of distribution networks. Model-based approaches tend to work well only if detailed network parameters are available, while data-driven approaches can suffer from overfitting and may not generalize well. We created the PHY-GAT-SAC framework to address these issues. Physics-regularized reinforcement learning uses bidirectional graph attention, which combines a physics-informed model with a safety projection method that relies on sensitivity matrices. This makes it so that the voltage regulation is practical, interpretable, and secure. The framework works with two combined branches. One branch takes care of the nonlinear mapping from power injections to voltage states using a forward graph encoder and a reverse consistency constraint. At the same time, another branch extracts features directly from the voltages to improve the perception of system violation risk. The framework has a sensitivity-based safety layer as well. This layer projects every control action into a feasible area formed by linearized voltage restrictions, thus securing operation safety. Experiments on an IEEE 33-node system show that the framework works well. A safety layer guarantees a safe operating range without exact impedance values. And PHY-GAT-SAC greatly lowers voltage violations compared to multi-agent deep reinforcement learning. By successfully combining physics with learning, this study gives a unified framework for merging graph neural networks and reinforcement learning within intricate grid management. Full article

The Hall–Héroult aluminum electrolysis process remains highly energy-intensive, making energy efficiency improvement crucial for sustainable aluminum production. Recent progress has focused on four key areas: electrolyzer structure optimization, advanced electrode materials, intelligent process control, and waste heat recovery. Structural innovations such as reducing the anode to cathode distance (ACD) and improving magnetohydrodynamic stability have lowered operating voltage and thermal losses. Novel carbon-based and conductive electrode materials have improved current efficiency and extended service life. Intelligent control methods, including model predictive control, adaptive dynamic programming, and Kalman filtering, have optimized alumina feeding, stabilized operations, and reduced perfluorocarbon emissions. Moreover, recovering waste heat from anode gases and electrolyzer sidewalls has created new opportunities for energy reuse. The integration of these strategies is advancing aluminum electrolysis toward higher efficiency, lower carbon emissions, and intelligent operation. Future directions include digital twin modeling, artificial-intelligence-driven control, ultra-low ACD designs, and efficient heat recovery systems to promote sustainable industrial transformation. Full article

As the mainstream technology solution for deep imaging LiDAR, dToF measurement has been widely applied in emerging fields such as environmental perception and obstacle recognition, 3D terrain reconstruction, real-time motion capture, and drone obstacle avoidance navigation due to its advantages of high resolution, long-range detection capability, and high sensitivity. In order to adapt to functional applications in different scenarios, the resolution of TDC needs to be adjustable and can work normally in different environments. In view of this, this article studies the pixel array and TDC circuit in the chip and locks a voltage-controlled ring oscillator (VCRO) with the same structure as the pixel to a fixed frequency through a PLL structure. Then copy the control voltage of the locked VCRO to the control terminal of the TDC in each pixel. In an ideal situation, this control voltage can make the oscillation frequency of VCRO within the pixel consistent with the locking frequency of VCRO within the PLL, and insensitive to changes in PVT. This study developed a module expandable 16 × 16-pixel array dToF sensor chip based on TDC architecture using CMOS technology. Finally, six configurable 16 × 16-pixel subarrays were integrated and constructed into a 32 × 48 large-scale dToF sensor chip through modular splicing. The top-level layout design was completed using SMIC 180 nm technology, with a layout area of 5285 µm × 3669 µm. Post-simulation verification showed that, under the testing conditions of a 400 MHz system clock and a 33.3 kHz frame rate, the dToF chip system performance indicators were: time measurement resolution of 156 ps, DNL < 1 LSB, INL < 0.85 LSB, and absolute ranging accuracy better than 2.5 cm. Full article

As integrated circuits face increasingly stringent demands regarding power consumption, area, and stability, integrating novel spintronic devices with computing architectures has become a crucial direction for breaking through traditional computing paradigms. In the paper, switching mechanism of Magnetic Tunnel Junctions (MTJs) under the synergistic effect of Voltage-Controlled Magnetic Anisotropy (VCMA) and the Spin Hall Effect (SHE) is investigated. VCMA-assisted switching SHE-MTJ device is adopted, and a macrospin approximation model is established based on the Landau-Lifshitz-Gilbert (LLG) equation to systematically analyze its dynamic characteristics. The research demonstrates that applying VCMA voltage pulses with appropriate amplitude and width can significantly reduce the required spin Hall current density and pulse width for switching, thereby effectively minimizing ohmic losses and Joule heating. Furthermore, by incorporating a thermal fluctuation field, voltage-controlled SHE-MTJ device with stochastic switching behavior can be constructed, obtaining an approximately sigmoidal voltage-probability response curve. This provides an ideal physical foundation for stochastic computing and neuromorphic computing. Based on the above established fundamental discovery, an in-memory computing architecture supporting binarized Convolutional Neural Networks (CNNs) is proposed and designed in the paper. Combined with the lightweight network SqueezeNet, this architecture achieves a Top-1 recognition accuracy of 72.49% on the CIFAR-10 dataset, with a parameter count of only 1.25 × 10⁶. This work offers a feasible spintronic implementation scheme for low-power, high-energy-efficiency edge-side intelligent chips. Full article

This article presents the analysis of selected maximum power point tracking (MPPT) algorithms and their influence on developed energy harvester (EH) systems under uniform conditions. The energy harvester is an electronic system that converts available ambient energy to electrical energy and regulates its distribution to the output. The aim is to design an energy harvester with the highest integration rate possible with consideration of area requirements and low power consumption. To improve the overall energy conversion of the developed harvester, we implemented several MPPT algorithms (Pilot Cell, Constant Voltage, Perturb and Observe) into a dedicated MPPT controller that controls the DC-DC converter. Consequently, we experimentally analyzed their impact on the harvester system. Findings show that even simple algorithms with smaller chip areas and lower power consumption can achieve results comparable to more complex ones. The proposed, manufactured and experimentally evaluated EH chip prototype has proven its expected functionality and is therefore fully capable of supplying energy for low-power electronics and battery-operated devices. Full article

With the accelerated low-carbon transition of the global energy mix, offshore wind power (OWP) is one of the fastest-growing renewable resources and is often integrated with conventional thermal units into a bundled export transmission system. Under sudden large disturbances, the lack of inertia support makes rotor-angle instability prone to occur, which undermines sustainable operation. Battery energy storage systems (BESS) provide fast emergency power support, and an effective control strategy can enhance transient rotor-angle stability while improving operational sustainability. Accordingly, equivalent-circuit models of the regional export system are established for the before-fault, during-fault, and after-fault stages. Building on the extended equal area criterion (EEAC) and the low-voltage ride-through (LVRT) capability of OWP, the stabilizing mechanism of BESS participation is examined from the perspectives of optimal power and timing, thereby yielding an optimal BESS control strategy for improving transient rotor-angle stability in regional renewable export systems. Finally, a regional renewable export system is implemented in MATLAB/Simulink R2022b, where severe contingencies are imposed to validate the effectiveness of the proposed BESS control strategy. Full article

Triboelectric nanogenerators (TENGs) have gradually been applied in various practical scenarios, mainly focusing on core areas such as wearable motion monitoring devices, medical security systems, and natural resource exploration technology. However, they have the problem of low output energy and have not yet formed effective integration with mature commercially available products, which has hindered the industrialization process. This situation still significantly limits its global promotion and application. In this study, TENG was used as the sensing module for intelligent automotive airbags. We tested the voltage and current output characteristics of the system under different impact forces and frequency conditions. During the testing process, the electrical energy generated under different operating conditions is transmitted to the control system via Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) circuits. The system will quickly determine whether to trigger the airbag deployment based on the received electrical signals, and activate the ignition device when necessary to achieve rapid inflation and deployment of the airbag. Compared with traditional triggering mechanisms, the airbag system based on this designed sensor has higher sensitivity and reliability. The sensor can stably capture collision signals, and experiments have shown that as the collision speed increases, the slope of its open-circuit voltage gradually approaches infinity. Applying TENG to automotive airbags not only effectively improves the triggering efficiency and accuracy of airbags, but also provides more reliable safety protection for drivers and passengers. Finite element simulation of the automotive airbag was conducted to provide specific data support for evaluating its safety performance. With the continuous advancement of TENG technology and further expansion of its application scenarios, we believe that such innovative safety technologies will play a more critical role in the future automotive industry. Full article