Search Results (2,710)

The integration of solar energy into smart grids requires high-efficiency power conversion to support grid stability. However, Partial Shading Conditions (PSCs) remain a primary obstacle by inducing multiple local maxima on P–V characteristic curves. This paper presents a hardware-aware and memory-enhanced Maximum Power Point Tracking (MPPT) approach based on a modified Dragonfly Algorithm (DA) for grid-connected microinverter-based photovoltaic (PV) systems. The proposed method utilizes a quasi-switched Boost-Switched Capacitor (qSB-SC) topology, where the DA is specifically tailored by combining Lévy-flight exploration with a dynamic damping factor to suppress steady-state oscillations within the qSB-SC ripple constraints. Coupling the MPPT stage to a seven-level Packed-U-Cell (PUC) microinverter ensures that each PV module operates at its independent Global Maximum Power Point (GMPP). A ZigBee-based Wireless Sensor Network (WSN) facilitates rapid data exchange and supports ‘swarm-memory’ initialization, matching current shading patterns with historical data to seed the population near the most probable GMPP region. This integration reduces the overall response time to 0.026 s. Hardware-in-the-loop experiments validated the approach, attaining a tracking accuracy of 99.32%. Compared to current state-of-the-art benchmarks, the proposed model demonstrated a significant improvement in tracking speed, outperforming the most recent 2025 GWO implementation (0.0603 s) by approximately 56% and conventional metaheuristic variants such as GWO-Beta (0.46 s) by over 94%.These results confirmed that the modified DA-based MPPT substantially enhanced the microinverter efficiency under PSC through cross-layer parameter adaptation. Full article

To maintain power system stability and supply quality when integrating doubly fed induction generator (DFIG)-based wind energy conversion systems (DFIG-WECSs), regulators regularly update grid codes specifying low voltage ride-through (LVRT) requirements. This paper presents a systematic literature review (SLR) on the use of STATCOMs to enhance LVRT capability in DFIG-WECSs. Objectives included a structured literature search, bibliographic analysis, thematic synthesis, trend identification, and proposing future research directions. A PRISMA-based methodology guided the review, utilising PRISMA 2020 for Abstracts in the development of the abstract. The final search was conducted on Scopus (31 March 2025). Eligible studies were primary research in English (2009–2014) where STATCOM was central to LVRT enhancement; exclusions included non-English studies, duplicates, reviews, and studies without a STATCOM focus. Quality was assessed using an adapted Critical Appraisal Skills Programme (CASP) tool. No automation or machine learning tools were used. Thirty-eight studies met the criteria and were synthesised under four themes: operational contexts, STATCOM-based schemes, control strategies, and optimisation techniques. Unlike prior reviews, this study critically evaluates merits, limitations, and practical challenges. Trend analysis shows evolution from hardware-based fault survival strategies to advanced optimisation and coordinated control schemes, emphasising holistic grid stability and renewable integration. Identified gaps include cyber-physical security, techno-economic assessments, and multi-objective optimisation. Actionable research directions are proposed. By combining technical evaluation with systematic trend analysis, this review clarifies the state of STATCOM-assisted LVRT strategies and outlines pathways for future innovation in DFIG-WECS integration. Full article

The increasing penetration of wind energy has led to reduced system inertia and heightened sensitivity to dynamic disturbances in modern power systems. This paper proposes a Newton–Raphson-Based Optimizer (NRBO) for tuning proportional, integral, and feedforward gains of a grid-forming converter applied to a wind energy conversion system operating in a low-inertia environment. The study considers an aggregated wind farm modeled as a single equivalent DFIG-based wind turbine connected to an infinite bus, with detailed dynamic representations of the converter control loops, synchronous generator dynamics, and network interactions formulated in the dq reference frame. The grid-forming converter operates in a grid-connected mode, regulating voltage and active–reactive power exchange. The NRBO algorithm is employed to optimize a composite objective function defined in terms of voltage deviation and active–reactive power mismatches. Performance is evaluated under two representative scenarios: small-signal disturbances induced by wind torque variations and short-duration symmetrical voltage disturbances of 20 ms. Comparative results demonstrate that NRBO achieves lower objective values, faster transient recovery, and reduced oscillatory behavior compared with Differential Evolution, Particle Swarm Optimization, Philosophical Proposition Optimizer, and Exponential Distribution Optimization. Statistical analyses over multiple independent runs confirm the robustness and consistency of NRBO through significantly reduced performance dispersion. The findings indicate that the proposed optimization framework provides an effective simulation-based approach for enhancing the transient performance of grid-forming wind energy converters in low-inertia systems, with potential relevance for supporting stable operation under increased renewable penetration. Improving the reliability and controllability of wind-dominated power grids enhances the delivery of cost-effective, cleaner, and more resilient energy systems, aiding in expanding sustainable electricity access in alignment with SDG7. Full article

Energy management systems incorporating battery energy storage systems (BESSs) are an effective way to deal with peak power demand in power systems, contributing to sustainability and energy management. In these systems, BESS interface converters encounter many challenges, such as achieving high efficiency, reliability, and scalability. A partial power processing (PPP) converter is one of the promising methods for reducing system energy losses and increasing reliability, thereby optimizing energy conversion between the BESS and the dc bus. However, implementing PPP in a BESS requires a high input-to-output voltage ratio and bidirectional operation over the battery voltage variation, which increases the design complexity. This paper proposes a half-bridge push–pull converter and provides guidance on its design for that purpose. A design procedure considering system efficiency and operating principles is comprehensively investigated and is applied to a PPP converter operating with a 15 kW BESS. The simulation and experimental results show that the efficiency of the system reaches up to 99% with a converter efficiency of 88%, which shows that the proposed structure achieves bidirectional operation and high efficiency based on the partial power processing concept. Full article

Due to the Von Neumann bottleneck of traditional CMOS computing, there is an urgent need to develop in-memory logic devices with low power consumption. In this work, we demonstrate ferroelectric diode devices based on the TiN/Hf_0.5Zr_0.5O₂/HfO₂/TiN structure, implementing 16 Boolean logic operations through single-step or multi-step (2–3 steps) cascade and achieving attojoule-level one-bit full-adder computation. The TiN/Hf_0.5Zr_0.5O₂/HfO₂/TiN ferroelectric diode exhibits non-destructive readout and bidirectional rectification characteristics, with the conduction mechanism following Schottky emission behavior in the on-state. Based on its bidirectional rectification characteristics, we designed and simulated the circuit scheme of 16 Boolean logic and one-bit full-adder through cascaded operations. Both the input and output logic values are represented in the form of resistance, without the need for additional form conversion circuits. The state writing is performed by pulse-controlled polarization flipping, and the state reading is non-destructive. The logic circuits in this work demonstrate superior performance with ultralow computing power consumption in simulation. This breakthrough establishes a foundation for developing energy-efficient and scalable in-memory computing systems. Full article

The increasing energy crisis demands sustainable functional materials. Wood, with its natural three-dimensional porous structure, offers an ideal renewable template. This study demonstrates that microstructural engineering of wood is a decisive strategy for enhancing magnetothermal conversion. Using eucalyptus wood, we precisely tailored its pore architecture via delignification and synthesized Fe₃O₄ nanoparticles in situ through coprecipitation. We systematically investigated the effects of delignification and precursor immersion time (24, 48, 72 h) on the loading, distribution, and magnetothermal performance of the composites. Delignification drastically increased wood porosity, raising the Fe₃O₄ loading capacity from ~5–6% (in non-delignified wood) to over 14%. Immersion time critically influenced nanoparticle distribution: 48 h achieved optimal deep penetration and uniformity, whereas extended time (72 h) induced minor local agglomeration. The optimized composite (MDW-48) achieved an equilibrium temperature of 51.2 °C under a low alternating magnetic field (0.06 mT, 35 kHz), corresponding to a temperature rise (ΔT) > 24 °C and a Specific Loss Power (SLP) of 1.31W·g⁻¹. This performance surpasses that of the 24 h sample (47 °C, SLP = 1.16 W·g⁻¹) and rivals other bio-based magnetic systems. This work establishes a clear microstructure–property relationship: delignification enables high loading, while controlled impregnation tunes distribution uniformity, both directly governing magnetothermal efficiency. Our findings highlight delignified magnetic wood as a robust, sustainable platform for efficient low-field magnetothermal conversion, with promising potential in low-carbon thermal management. Full article

This study presents a modular battery charging system based on a DC–DC buck converter with proportional–integral (PI) control, developed to support hands-on learning in power electronics education. In response to the need for flexible experimental platforms, the system is designed to bridge theoretical concepts of power conversion and control with practical implementation. The proposed setup employs cascaded current and voltage control loops to achieve constant current (CC) and constant voltage (CV) charging modes, while its modular hardware architecture allows modification of key parameters such as inductance, capacitance, and circuit topology. The control algorithms are implemented on a microcontroller, and real-time data acquisition is integrated using the ThingSpeak platform for monitoring system behaviour. Experimental results show that the current control loop recovers to its reference value within approximately 6 ms under abrupt load variations, whereas the voltage control loop settles within approximately 15 ms, demonstrating stable closed-loop performance. In addition, the system successfully charges a 12 V lead-acid battery following a standard CC–CV charging profile. Overall, the proposed experiment kit provides an effective educational platform and a practical basis for further exploration of battery charging strategies and power converter control. Full article

Electrochemical CO₂ reduction (CO₂RR) is a promising route to transform a major greenhouse gas into value-added fuels and chemicals. However, its deployment is still hindered by the sluggish activation of CO₂, poor selectivity toward multielectron products, and competition with the hydrogen evolution reaction (HER). Single-atom catalysts (SACs) have emerged as powerful materials to address these challenges because they combine maximal metal utilization with well-defined coordination environments whose electronic structure can be precisely tuned through metal–support interactions. This minireview summarizes current understanding of how structural, electronic, and chemical features of SAC supports (e.g., porosity, heteroatom doping, vacancies, and surface functionalization) govern the adsorption and conversion of key CO₂RR intermediates and thus control product distributions from CO to CH₄, CH₃OH and C₂⁺ species. Particular emphasis is placed on selectivity descriptors (e.g., coordination number, d-band position, binding energies of *COOH and *OCHO) and on rational design strategies that exploit curvature, microenvironment engineering, and electronic metal–support interactions to direct the reaction along desired pathways. Representative SAC systems based primarily on N-doped carbons, complemented by selected examples on oxides and MXenes are discussed in terms of Faradaic efficiency (FE), current density and operational stability under practically relevant conditions. Finally, the review highlights remaining bottlenecks and outlines future directions, including operando spectroscopy and data-driven analysis of dynamic single-site ensembles, machine-learning-assisted DFT screening, scalable mechanochemical synthesis, and integration of SACs into industrially viable electrolyzers for carbon-neutral chemical production. Full article

The proliferation of Internet of Things (IoT) devices has significantly expanded the threat landscape for malicious software (malware), rendering traditional signature-based detection methods increasingly ineffective in coping with the volume and evolving nature of modern threats. In response, researchers are utilising artificial intelligence (AI) for a more dynamic and robust malware detection solution. An innovative approach utilising AI is focusing on image classification techniques to detect malware on resource-constrained Single-Board Computers (SBCs) such as the Raspberry Pi. In this method the conversion of malware binaries into 2D images is examined, which can be analysed by deep learning models such as convolutional neural networks (CNNs) to classify them as benign or malicious. The results show that the image-based approach demonstrates high efficacy, with many studies reporting detection accuracy rates exceeding 98%. That said, there is a significant challenge in deploying these demanding models on devices with limited processing power and memory, in particular those involving of both calculation and time complexity. Overcoming this issue requires critical model optimisation strategies. Successful approaches include the use of a lightweight CNN architecture and federated learning, which may be used to preserve privacy while training models with decentralised data are processed. This hybrid workflow in which models are trained on powerful servers before the learnt algorithms are deployed on SBCs is an emerging field attacting significant interest in the field of cybersecurity. This paper synthesises the current state of the art, performance compromises, and optimisation techniques contributing to the understanding of how AI and image representation can enable effective low-cost malware detection on resource-constrained systems. Full article

Carbon capture, utilization, and storage (CCUS) play an increasingly important role in climate mitigation strategies by addressing industrial emissions and enabling pathways toward net-negative emissions. A key challenge lies in determining the pathway of captured CO₂, whether through permanent geological storage or conversion into value-added products to enhance system viability. As hard-to-abate sectors and the power industry remain major sources of emissions, a comprehensive assessment of the technical, environmental, and economic performance of CCUS pathways is essential. This study evaluates bioenergy with carbon capture and storage/utilization (BECCUS) in the context of the Polish energy sector. Techno-environmental performance was assessed across three pathways: CO₂ storage in saline formations, CO₂ mineralization, and methanol synthesis. The results show levelized costs of 59.9 EUR/tCO_2,in for storage, 109.7 EUR/tCO_2,in for mineralization, and 631.1 EUR/tCO_2,in for methanol production. Corresponding carbon footprints (including full chain emissions) were −936.4 kgCO_2-eq/tCO_2,in for storage, −460.6 kgCO_2-eq/tCO_2,in in for mineralization, and 3963.4 kgCO_2-eq/tCO_2,in for methanol synthesis. These values highlight the trade-offs between economic viability and climate performance across utilization and storage options. The analysis underscores the potential of BECCS to deliver net-negative emissions and supports strategic planning for CCUS deployment in Poland. Full article

Wireless Power Transfer (WPT) has become a pivotal technology, enabling the battery-free operation of Internet of Things (IoT) and biomedical devices while supporting environmental sustainability. This review provides a comprehensive analysis of microstrip patch antennas (MPAs) operating at the 5.8 GHz Industrial, Scientific, and Medical (ISM) band, emphasizing their advantages over the more commonly used 2.4 GHz band. A detailed and systematic classification framework for MPA architectures is introduced, covering single-element, multi-band, ultra-wideband, array, MIMO, wearable, and rectenna systems. The review examines advanced optimization methodologies, including Defected Ground Structures (DGS), Electromagnetic Bandgap (EBG) structures, Metamaterials (MTM), Machine Learning (ML), and nanomaterials, each contributing to improvements in gain, bandwidth, efficiency, and device miniaturization. Unlike previous surveys, this work offers a performance-benchmarked classification specifically for 5.8 GHz MPAs and provides a quantitative assessment of key trade-offs, such as efficiency versus substrate cost. The review also advocates for a shift toward Power Conversion Efficiency (PCE)-centric co-design strategies. The analysis identifies critical research gaps, particularly the ongoing disparity between simulated and experimental performance. The review concludes by recommending multi-objective optimization, integrated antenna-rectifier co-design to maximize PCE, and the use of advanced materials and computational intelligence to advance next-generation, high-efficiency 5.8 GHz WPT systems. Full article

Symmetry and asymmetry are foundational design principles in artificial intelligence, defining the balance between invariance and adaptability in multimodal learning systems. In audio-visual speaker verification, where speech and lip-motion features are jointly modeled to determine whether two utterances belong to the same individual, these principles govern both fairness and discriminative power. In this work, we analyze how symmetry and asymmetry emerge within a gated-fusion architecture that integrates Time-Delay Neural Networks and Bidirectional Long Short-Term Memory encoders for speech, ResNet-based visual lip encoders, and a shared Conformer-based temporal backbone. Structural symmetry is preserved through weight-sharing across paired utterances and symmetric cosine-based scoring, ensuring verification consistency regardless of input order. In contrast, asymmetry is intentionally introduced through modality-dependent temporal encoding, multi-head attention pooling, and a learnable gating mechanism that dynamically re-weights the contribution of audio and visual streams at each timestep. This controlled asymmetry allows the model to rely on visual cues when speech is noisy, and conversely on speech when lip visibility is degraded, yielding adaptive robustness under cross-modal degradation. Experimental results demonstrate that combining symmetric embedding space design with adaptive asymmetric fusion significantly improves generalization, reducing Equal Error Rate (EER) to 3.419% on VoxCeleb-2 test dataset without sacrificing interpretability. The findings show that symmetry ensures stable and fair decision-making, while learnable asymmetry enables modality awareness together forming a principled foundation for next-generation audio-visual speaker verification systems. Full article

The single-step Rydberg excitation of cesium atoms requires a 319 nm ultraviolet laser with a narrow laser linewidth, high frequency stability, and high output power. To meet these requirements, in this work, we construct a high-power, single-frequency UV laser system at this wavelength. In this system, the frequency stabilization of the 1560.492 nm seed laser is critical to the performance of the ultraviolet laser. We employ nonlinear frequency conversion technology, the 1560.492 nm laser is frequency-doubled to 780.246 nm via a single pass through a PPLN crystal, and function integration is realized based on the modular parameter adjustment interface provided by the PyRPL software. Subsequently, the 1560.492 nm laser is stabilized to the $D_2$ hyperfine transition line of Rb-87 atoms using polarization spectroscopy (PS) and radio-frequency-modulated saturation absorption spectroscopy (RF-SAS). A comparative study of these two techniques shows that RF-SAS achieves superior stabilization performance, with the residual frequency fluctuation of the frequency-doubled laser being 1.07 MHz over 30 min. According to frequency doubling theory, the actual residual frequency fluctuation of the 1560.492 nm fundamental-frequency laser can be calculated as 0.535 MHz. Compared with our earlier scheme that utilized an ultra-low-expansion (ULE) optical cavity as a frequency reference, the present scheme eliminates the long-term drift induced by environmental factors. In contrast to frequency stabilization relying on discrete instruments, this integrated scheme significantly reduces the cost, simplifies the system architecture, saves space, and greatly enhances the flexibility and controllability of the system. It therefore provides a reliable and cost-effective solution to ensure the portability and practicability of high-performance UV laser sources. This high-precision frequency stabilization scheme directly guarantees the performance of the 319 nm UV laser, suppressing its linewidth below 10 kHz. Thus, it fully meets the stringent laser linewidth and frequency stability requirements for the single-step Rydberg excitation of cesium atoms and provides a reliable light source foundation for subsequent precision spectroscopic measurements. Full article

The widespread adoption of low-power devices and microelectronic systems has intensified the need for efficient energy harvesting solutions. While cantilever-beam piezoelectric energy harvesters (PEHs) are popular for their simplicity, their performance is often limited by conventional mass block designs. This study addresses this by proposing a comprehensive structural optimization framework for a triangular cantilever PEH to significantly enhance its electromechanical conversion efficiency. The methodology involved a multi-stage approach: first, an embedded coupling design was introduced to connect the mass block and cantilever beam, improving space utilization and strain distribution. Subsequently, the mass block’s shape was optimized. Furthermore, a negative Poisson’s ratio (NPR) honeycomb structure was integrated into the cantilever beam substrate to induce biaxial strain in the piezoelectric layer. Finally, a variable-density mass block was implemented. The synergistic combination of all optimizations—embedded coupling, NPR substrate, and variable-density mass block—culminated in a total performance enhancement of 69.07% (17.76 V) in voltage output and a 44.34% (28.01 Hz) reduction in resonant frequency. Through experimental testing, the output performance of the prototype machine showed good consistency with the simulation results, successfully verifying the effectiveness of the structural optimization method proposed in this study. These findings conclusively show that strategic morphological reconfiguration of key components is highly effective in developing high-performance, low-frequency adaptive piezoelectric energy harvesting systems. Full article

Silicon carbide (SiC) power converters are increasingly used in automotive, renewable energy, and industrial applications. While reliability assessments are typically performed at either the device or system level, an integrative approach that simultaneously evaluates both levels remains underexplored. This article presents a novel system-level simulation method with two strategies to evaluate the reliability of power devices and a resonant converter under varying temperatures and total ionizing doses (TIDs). Temperature-sensitive electrical parameters (TSEPs), such as on-state resistance ($R_{\mathrm{ON}}$) and threshold voltage shift ($\Delta V_{\mathrm{TH}}$), are calibrated and analyzed using a B1505A curve tracer. These parameters are incorporated into the system-level simulation of a 300 W resonant converter with a boosting cell. Both Silicon (Si) and SiC-based power resonant converters are assessed for power application in space engineering and harsh environments. Additionally, gate-oxide degradation and $\Delta V_{\mathrm{TH}}$-related issues are discussed based on the simulation results. The thermal-strategy results indicate that SiC MOSFETs maintain a more stable conduction loss at elevated temperatures, exhibiting higher reliability due to their high thermal conductivity. Conversely, increased TIDs result in a negative shift in conduction losses across all SiC devices under the radiation strategy, affecting the long-term reliability of the power converter. Full article