Search Results (418)

Accurate power prediction is essential for assessing wind turbine performance under real-world operating conditions and for supporting condition monitoring and maintenance planning using SCADA data. Most existing approaches rely directly on raw SCADA signals, which may limit their ability to capture complex spatiotemporal dependencies among operational variables. To address this limitation, this paper proposes a novel SCADA-driven power prediction framework that transforms selected SCADA variables into multi-channel grayscale images and leverages an optimized LeNet-5–LSTM hybrid neural network for active and reactive power prediction. First, the SCADA dataset is analyzed to identify the most influential variables affecting power output. Six key variables are then selected, segmented, and encoded as 2D grayscale images, enabling the model to learn richer feature representations compared to conventional raw SCADA data-based methods. The proposed network combines convolutional layers for spatial feature extraction from SCADA data-based grayscale images with LSTM layers to capture temporal dependencies. Model training incorporates a customized loss function that integrates both data-driven supervision and physics-based constraints. The model is trained using 70% of the image-based dataset, with five independent runs to ensure robustness and reproducibility, while the remaining 30% is used for testing. The proposed approach is validated using SCADA data from three real-world cases: (i) a 2 MW Siemens wind turbine in Poland, (ii) a Vestas V52 wind turbine in Ireland, and (iii) the La Haute Borne wind farm in France, consisting of four wind turbines. The results demonstrate that the SCADA-based image representation enables the proposed LeNet-5–LSTM model to effectively learn discriminative feature patterns and achieve accurate active and reactive power predictions across different turbine types and operating conditions. Full article

The high-penetration integration of renewable energy into port power systems is challenged by the stochastic volatility of wind–solar generation and dynamic load demands. To address this, this study proposes a data-driven bi-level coordinated planning framework for port wind–solar-storage systems, integrating a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and hybrid secretary bird optimization algorithm (SBOA) for solution seeking. The WGAN-GP-K-Means++ framework is adopted to capture the high-dimensional spatiotemporal correlations under the uncertainty of source ports and loads, and to generate the wind and solar resource scenarios for typical day. Subsequently, a bi-level planning model is constructed: the upper layer optimizes the siting and sizing of distributed generation and energy storage to minimize the life-cycle net present value, while the lower layer minimizes annual operating costs through multi-scenario dispatch. To resolve the resulting complex mixed-integer programming problem, a nested SBOA-Gurobi algorithm is developed. Case study of a Guangxi port demonstrates that the proposed approach reduces life-cycle cost by 44.94% relative to the baseline grid-connected scheme and exhibits superior convergence stability compared with GA, GRSO, and WOA. Additionally, sensitivity analysis quantifies the impact of electricity pricing policies, shore power utilization rates, and discount rate on the system’s economic benefits. This study provides a decision-support tool for the low-carbon transition and economic planning of port energy systems. Full article

The policymakers of Bangladesh have been mapping the energy mix to shift its high dependency on fossil fuels to sustainable energy; wind energy is addressed as a highly potential option. A feasible site selection process is essential for wind power plant establishment; thus, this study aims to identify potential areas for the sustainable development of large-scale wind plants by considering socio-economic, safety and environmental factors. In this study, two techniques of multi-criteria analysis (MCA), analytical hierarchy process (AHP) and ratio scale weighting (RSW), were incorporated with geographic information system (GIS) to select the optimal area in Bangladesh. This study considers fifteen sub-criteria under four main criteria, namely, socio-economy, geology, ecology, and climatology. AHP and RSW assign suitable weights to the sub-criteria based on their significant impact on the plant. GIS analyzes spatial data layers and produces suitability maps with the following categories: 5—most suitable, 4—suitable, 3—moderately suitable, 2—unsuitable, 1—completely unsuitable, and 0—excluded area. The final suitability map was generated using suitability maps of AHP and RSW. Finally, a combination of the final suitability map and the wind speed suitability map provide a total suitable area of 1595.8293 km². This could produce 2.96 GW power with 1418 wind turbines and be able to reduce 4,992,346.42 tons of CO₂ emissions annually (calculated using a reference turbine). The study was uniquely carried out at a 150 m hub height, and integration of AHP and RSW for weight cross-validation was performed for the first time in large-scale wind plant siting in Bangladesh. The findings of the study can be helpful for decision-makers in developing large-scale wind power plants. Full article

This study investigates the characterisation of particulate matter (PM) across different size fractions (TSP, PM₁₀, PM₄, PM_2.5, and PM₁) in Porto, Portugal, over a 2-year period. Sampling was conducted at two heights (ground level and rooftop), integrating real-time measurements and filter-based analyses to evaluate seasonal and spatial variations. Elemental composition was determined using Inductively Coupled Plasma–Mass Spectrometry (ICP-MS), enabling detailed assessments of 30 chemical elements. Meteorological parameters, including temperature, precipitation, wind speed, and direction, were analysed to understand their influence on PM concentrations. Results indicate that significant seasonal trends, with higher PM concentrations observed during autumn and winter, were associated with low boundary layer height, promoting greater mixing of particles, enhanced deposition, and higher anthropogenic emissions, with average seasonal TSP values ranging from 0.001 to 0.059 µg m⁻³. Elemental analysis revealed distinct profiles at ground and rooftop levels, with Ba, Cu, Pb, Mg, and Na among the most frequently detected elements; ground-level samples showed stronger contributions from local sources, such as traffic, while rooftop samples reflected regional and long-range transport. Meteorological factors, such as precipitation and wind speed, exhibited negative correlations with PM concentrations, underscoring their role in atmospheric washing. These findings highlight the complex interplay of local and regional factors in shaping PM dynamics and emphasise the importance of multi-level monitoring for effective air-quality management. Full article

This paper presents a multi-layer architecture designed to enhance the reliable autonomous flight of single and multiple quadcopters in simulation. The architecture leverages concepts inspired by the resilient spacecraft executive to hierarchically organize trajectory planning and flight control and integrates an extended Simplex framework that employs multiple candidate algorithms to provide safety assurance at each layer, with a supervisory program that adapts Simplex behavior based on system states and environmental conditions to enable high-level mission management. The approach is evaluated in bridge-inspection simulations under environmental uncertainties, including varying wind conditions and obstacles. Across multiple operating configurations and Monte Carlo simulation runs, the architecture achieves high coverage rates; notably, under high-wind conditions, it reduces average trajectory deviation by 66.2%. The results demonstrate proactive safety through graceful degradation in both trajectory planning and flight control under stress and off-nominal conditions. Full article

Twin wildfires burned over 9500 ha in Seferihisar, İzmir, western Türkiye, on 29—30 June 2025 under extreme fire weather conditions. This study reconstructs the spatiotemporal progression of the fires and examines the drivers of contrasting behaviors and burn severity. Multi-source datasets—Sentinel-2 imagery, VIIRS/MODIS thermal detections, MTG images and thermal detections, aerial photos, and ground data—were integrated to delineate progression polygons and compute rate of spread (ROS), fuel consumption (FC), and fire-line intensity (FI). Kuyucak fire showed rapid early growth, burning 3554 ha in 2.5 h (mean ROS of 5.0 km h⁻¹; mean FI of 37,789 kW m⁻¹), driven by strong northeasterly winds of 40–50 km h⁻¹, steep terrain, dense Pinus brutia fuels, and very low dead fine-fuel moisture (<6%). Kavakdere fire advanced more slowly (mean ROS of 1.6 km h⁻¹) across open grassland and cropland, yielding lower FC and FI. Synoptic analysis revealed a strong pressure-gradient-induced northeasterly wind regime linked to a mid-tropospheric geopotential height dipole between Central Europe and the Eastern Mediterranean, while WRF simulations indicated a dry boundary layer and enhanced low-level winds during peak spread. Sentinel-2 dNBR burn severity mapping showed substantial spatial variability tied to fuel and topography contrasts. Findings demonstrate how twin ignitions under similar weather conditions can produce divergent outcomes, underscoring the need for terrain- and fuel-aware strategies during extreme Mediterranean fire outbreaks. Full article

To address the inherent mismatch between the fluctuating power output of renewable energy and the continuous production requirements of ammonia in off-grid wind–solar–hydrogen–ammonia systems, this paper proposes a “day-ahead–intraday–real-time” multi-time-scale coordinated optimization scheduling strategy. In the day-ahead layer, Wasserstein Distributionally Robust Optimization (WDRO) is employed to determine a conservative and stable baseline plan for ammonia load under high uncertainty of wind and solar output. The intraday layer utilizes Model Predictive Control (MPC) with a 2-h prediction horizon and 15-min rolling steps to correct short-term forecast deviations. The real-time layer achieves minute-level power balancing through priority dispatch and deadband control. Furthermore, hydrogen storage tanks serve as a material buffer between hydrogen production and ammonia synthesis, with their state variables transmitting across layers to achieve flexible multi-time-scale coupling. Simulation results demonstrate that, although this strategy slightly reduces the theoretical maximum ammonia yield, it completely avoids load-shedding risks. Compared with the deterministic scheduling (Scheme 1), which suffers a net loss due to severe penalty costs, the proposed strategy achieves a positive daily profit of CNY 277,700, representing an absolute increase of CNY 429,300. Furthermore, it provides an additional daily profit of CNY 65,800 compared to the stochastic optimization approach (Scheme 2), demonstrating superior economic robustness in off-grid environments. Full article

This paper presents a disc-type stepper motor based on PCB technology. Aiming to provide a solution for the difficulty of torque enhancement in multi-pole PCB stepper motors under the limited wiring space of the PCB stator, a novel spiral winding configuration is proposed. Without increasing the number of PCB stator layers or the overall dimensions, an axially offset layout is employed to enlarge the coil flux-linkage area, thereby increasing the electromagnetic torque. Theoretical analysis and finite element simulation results show that the proposed winding achieves approximately 30% higher torque than conventional spiral windings. Meanwhile, to address the current fluctuation problem caused by the low-inductance characteristic resulting from the coreless PCB stator, the influence of current ripple on the microstepping drive of the stepper motor is analyzed. A series-inductor approach is adopted to suppress current fluctuation, and the optimal inductor value is selected through theoretical calculation and simulation, which effectively reduces the current ripple and significantly improves the microstepping performance. Finally, a prototype is fabricated and tested experimentally. The results indicate that the motor output torque reaches 46.4 mN·m, and the step-angle error under 16-microstep drive is within 0.25°, providing a feasible solution for the design and control of PCB stepper motors in compact spaces. Full article

The global shift toward decarbonized power systems is driving unprecedented penetration of variable renewable energy sources, especially wind and solar PV. Legacy grid architectures, built around centralized, dispatchable synchronous generation, are ill-suited to manage the bidirectional power flows, reduced inertia, and new stability constraints introduced by inverter-based resources. Existing research offers deep but fragmented insights into individual elements of this transition, such as advanced power electronics, microgrids, or market design, but rarely integrates them into a coherent architectural vision for resilient, high-renewable grids. This review closes that gap by synthesizing technical, architectural, and institutional perspectives into a unified framework for resilient grid design toward 2030 and beyond. First, it traces the evolution from traditional hierarchical grids to smart, prosumer-centric, and modular multi-layer architectures, highlighting the implications for reliability and resilience. Second, it critically examines the core technical challenges of high VRES penetration, including stability, power quality, protection, and operational planning in converter-dominated systems. Third, it reviews the enabling roles of advanced power electronics, hierarchical control and wide-area monitoring, microgrids, and hybrid AC/DC networks. Case studies from Germany, China, and Egypt are used to distil context-dependent pathways and common design principles. Building on these insights, the paper proposes a scalable multi-layer framework spanning physical, data, control, and regulatory/market layers. The framework is intended to guide researchers, planners, and policymakers in designing resilient, converter-dominated grids that are not only technically robust but also economically viable and socially sustainable. Full article

Amid China’s pursuit of the “dual carbon” goals, the development and large-scale integration of renewable energy have become a core pillar of the power system transition. However, the intermittency and uncontrollability of wind and photovoltaic (PV) power have intensified peak-regulation conflicts after large-scale grid integration. Traditional coal-fired units lack sufficient flexibility to accommodate renewable energy fluctuations, while their willingness to participate in deep peak shaving remains low due to high associated costs. Addressing these challenges requires both enhanced system-level peak-regulation flexibility and effective market incentives for thermal units. Motivated by the limitations of existing studies that often consider individual flexibility resources or deterministic market mechanisms in isolation, this study investigates a coordinated multi-resource peak-regulation framework combined with an optimized market-clearing mechanism for deep peak-shaving ancillary services. First, flexibility resources are classified, and the peak-regulation mechanisms of source–load–storage coordination and auxiliary service markets are analyzed. Second, a wind–PV–thermal–storage operation cost model is established, followed by a two-layer peak-regulation market-clearing model that explicitly accounts for wind–PV uncertainty. The upper-level model minimizes total system operating costs through the coordinated dispatch of demand response and energy storage, while the lower-level model minimizes power purchase costs under a unified marginal clearing price. In addition, an uncertainty modeling framework based on Information Gap Decision Theory (IGDT) is introduced to manage renewable generation uncertainty and support decision-making under different risk preferences. Case studies are conducted to verify the effectiveness of the proposed framework. The results show that: (1) synergistic peak shaving through energy storage and demand response reduces the system peak–valley difference from 460 MW to 387.87 MW and decreases wind–PV curtailment costs from 355,000 yuan to 15,700 yuan, thereby alleviating thermal unit pressure and improving renewable energy accommodation; (2) the unified marginal clearing price mechanism reduces total system operating costs by 41.07% and significantly lowers the frequency of deep peak shaving for thermal units, enhancing their participation willingness; and (3) the IGDT-based model effectively addresses wind–PV uncertainty by providing optimistic and pessimistic scheduling strategies under different deviation coefficients. These results confirm that the proposed framework offers an effective and flexible solution for coordinated peak shaving in power systems with high renewable energy penetration. Full article

In low-amplitude and low-frequency vibration environments, the energy harvesting efficiency of self-powered wireless sensor nodes is insufficient, limiting their long-term autonomous operation. To address this issue, a micro piezoelectric–electromagnetic hybrid energy harvester is designed, aiming to enhance energy capture efficiency through structural integration and parameter optimization. The study is conducted entirely through numerical simulations. A coaxial integrated architecture is adopted, combining a piezoelectric cantilever beam array with an electromagnetic induction module. The piezoelectric layer uses lead magnesium niobate–lead titanate (PMN-PT) solid solution material with a thickness of 0.2 mm. The electromagnetic module employs copper wire coils with a diameter of 0.08 mm, winding 1500–3000 turns, paired with N52-type neodymium–iron–boron (NdFeB) permanent magnets. To improve energy conversion efficiency, the optimization parameters include the length-to-thickness ratio of the cantilever beam, the mass of the tip mass, the number of coil turns, and the spacing of the permanent magnets. Each parameter is set at four levels for orthogonal experiments. A multi-physics coupling model is established using ANSYS Workbench 2023, covering structural dynamics, piezoelectric effects, and the electromagnetic induction module. The mesh size is set to 0.1 mm. The energy output characteristics are analyzed under vibration frequencies of 0.3–12 Hz and amplitudes of 0.2–1.0 mm. Simulation results show that the optimized hybrid harvester achieves 45% higher energy conversion efficiency than a single piezoelectric structure and 31% higher than a traditional separated hybrid structure within the 0.3–12 Hz low-frequency range. Under a 6 Hz frequency and 0.6 mm amplitude, the output power density reaches 3.5 mW/cm³, the peak open-circuit voltage is 4.1 V, and the peak short-circuit current is 1.3 mA. Under environmental conditions of 20–88% humidity and −15–65 °C temperature, the device maintains over 94% stability in energy output. After 1.2 million vibration cycles, structural integrity remains above 96%, and energy conversion efficiency decreases by no more than 5%. The proposed coaxial hybrid structure and multi-parameter orthogonal optimization method effectively enhance energy harvesting performance in low-amplitude, low-frequency environments. The simulation design parameters and analysis procedures provide a reference for the development of similar micro hybrid energy harvesters and support the performance optimization of self-powered wireless sensor nodes. Full article

Clouds are essential for regulating the hydrological cycle and Earth’s radiation budget, and their fluctuations over the Tibetan Plateau (TP) have a significant effect on both regional climate dynamics and global atmospheric circulation. Using ground-based Vaisala CL51 ceilometer data and Fengyun-4A (FY-4A) satellite observations from October 2020 to June 2022, this study examines cloud occurrence frequency (COF), cloud vertical structure (including cloud base height (CBH), cloud top height (CTH), and cloud layer stratification), and related macroscopic properties over the Large High Altitude Air Shower Observatory (LHAASO). CL51 and FY-4A had cloud occurrence rates of 43.7% and 37.7%, respectively, over the observation period, with a strong correlation coefficient of 0.82. Given the impact of clouds on Cherenkov light observations by the LHAASO Wide Field of view Cherenkov Telescope Array (WFCTA), we specifically evaluated the cloud occurrence during the operational periods of the LHAASO-WFCTA, finding rates of 34.2% (CL51) and 28.0% (FY-4A), with the lowest rates occurring in the early morning. Due to monsoonal moisture inflow and dry northeasterly winds, seasonal COF changes showed clear peaks in summer (78.8%) and minima in winter (24.8%). Seasonal differences existed in the diurnal COF patterns, with nocturnal prominence in summer/autumn and daytime dominance in spring/winter. The CBH showed daily oscillations, peaking at 18:00 (local solar time) and troughing at 08:00 (local solar time), with seasonal CBH minima in summer/autumn and maxima in spring/winter. Low- and mid-level clouds predominated, with clear diurnal cycles: low- and mid-level clouds rose from morning until midday, while high-level clouds appeared after dusk. Vertical cloud structures were predominantly single-layered (81%), with multi-layered complexity peaking in the summer due to convective activity. The CTH distributions showed unimodal patterns in the fall and winter (1.5–3 km), while in the summer, they showed multimodal extents (up to 12 km). These results improve LHAASO-WFCTA observational scheduling, enhance climate model parameterizations, and deepen our understanding of the dynamics of the TP cloud. Full article

Ports located within dense urban environments face a major challenge in achieving deep decarbonization without compromising the reliability and safety of critical maritime operations. This study develops and validates a resilience-oriented control and sizing typology for Hybrid Renewable Energy Systems (HRESs), supporting the transition of a medium-sized Mediterranean port toward a Nearly Zero Energy Port (nZEP). The framework integrates five years of measured electrical demand at 15 min resolution to capture stochastic load variability, seasonal effects, and safety-critical peak events. Thirty-five HRES configurations are simulated using HOMER Pro, assessing photovoltaic and wind generation combined with alternative Energy Storage System (ESS) technologies under two grid-interface control strategies: Net Metering (NM) and non-NM curtailment-based operation. Conventional Lead–Acid batteries are compared with inherently safer Vanadium Redox Flow Batteries (VRFBs), while autonomy constraints of 24 h and 48 h are imposed to represent operational resilience. System performance is evaluated through a multi-criteria framework encompassing economic viability (Levelized Cost of Energy), environmental impact (Lifecycle Assessment-based carbon footprint), and operational reliability. Results indicate that NM-enabled HRES architectures significantly outperform non-NM configurations by exploiting the external grid as an active balancing layer. The optimal NM configuration achieves a Levelized Cost of Energy of 0.063 €/kWh under a 24 h autonomy constraint, while reducing operational carbon intensity to approximately 70 gCO₂,eq/kWh, corresponding to a reduction exceeding 90% relative to baseline grid-dependent operation. In contrast, non-NM systems require substantial storage and generation oversizing to maintain resilience, resulting in higher curtailment losses and Levelized Cost of Energy values of 0.12–0.15 €/kWh. Across both control regimes, VRFB-based systems consistently exhibit superior robustness and safety performance compared to Lead–Acid alternatives. The proposed typology provides a transferable framework for resilient and low-carbon port microgrid design under real-world operational constraints. Full article

Against the backdrop of high-proportion renewable energy grid integration, modeling accuracy for substations incorporating wind and solar power is critical. Traditional modeling methods rely on theoretical parameters and lack sufficient accuracy. This study uses the 154 kV/23 kV Yeonggwang Substation in Jeollanam-do, South Korea (connected to three wind farms and three solar power plants, with 35 Micro-Phasor Measurement Unit (μPMU) measurement points deployed) as a case study. It investigates three-phase detailed modeling using Power System Computer Aided Design (PSCAD) and μPMU data-driven calibration. Based on substation topology and equipment parameters, a simulation model encompassing main transformers, transmission lines, renewable energy units, and loads was established. A hierarchical calibration system of “data preprocessing—parameter identification—iterative correction” was constructed, employing an iterative optimization strategy of “main grid layer—renewable energy layer—load layer.” A multi-objective optimization function centered on voltage, current, and power was developed. Verification results show that after calibration, the mean relative error rates (MRE) for voltage, current, active power and reactive power are 2.46%, 2.57%, 2.52% and 3.96% respectively, with mean error reduction rates (MERRs) of 80%, 82.75%, 81.33%, and 74.94% compared to pre-calibration values. The uniqueness of the calibration method proposed in this study lies in its use of actual μPMU measurement data to drive PSCAD model parameter calibration, achieving precise matching with the actual characteristics of the substation. This provides a reference method for modeling and digital twin construction of similar substations, demonstrating significant engineering application value. Full article

We investigate the multi-layer atmospheric impacts of Typhoon Hagibis (2019), which formed on 6 October, tracked across 12–35° N and 135–155° E, and made landfall on 12 October over the Izu Peninsula, central Honshu, Japan. We present a multi-layer study that involves the troposphere, stratosphere and upper ionosphere to examine the thermodynamic and electromagnetic coupling between these layers due to such extreme weather conditions. Using ERA5 reanalysis, we identify pronounced stratospheric temperature perturbations, elevated atmospheric gravity wave (AGW) potential energy, substantial spatiotemporal variability in the zonal (U) and meridional (V) wind components, relative humidity, and specific rainwater content throughout the cyclone’s evolution. Quantitatively, AGW potential energy increased from background levels of <5 J kg⁻¹ to >40 J kg⁻¹ near the cyclone core, while tropospheric wind anomalies reached $\pm 30$–40 m s⁻¹, accompanied by relative humidity values exceeding 90% and specific rainwater content up to $1.5\times {10}^{-3}$ kg kg⁻¹, indicative of vigorous moist convection and strong vertical energy transport. The ionospheric response, derived from GPS-based Total Electron Content (TEC) at 10 Japanese IGS stations, reveals vertical TEC (VTEC) perturbations whose amplitudes and temporal evolution vary systematically with GPS-station-to-typhoon-eye distance, including clear enhancements and reductions around the closest-approach day. These signatures indicate a measurable ionospheric response to cyclone-driven atmospheric forcing under geomagnetically quiet conditions, confirming that Hagibis produced vertically coupled disturbances linking stratospheric AGW activity with ionospheric electron density variability. Full article