Search Results (672)

Wind energy, an abundant renewable resource, remains difficult to harness efficiently due to fluctuating speeds and unpredictable directions. In this work, we present a wind bell-inspired triboelectric nanogenerator (WB-TENG) designed for omnidirectional, variable-speed wind harvesting, utilizing layered triboelectric polymers such as polytetrafluoroethylene (PTFE), polyethylene terephthalate (PET), and polyamide (PA) to enhance energy capture performance. The developed device demonstrates the ability to generate electrical output even under extremely low wind speeds as low as 0.5 m/s. Additionally, it successfully captures wind energy from all directions within a full 360° range. Through structural optimization, the WB-TENG achieves a peak output voltage of 25.1 V and a maximum power of 3.5 μW, representing substantial improvements of 170% and 1232%, respectively, over the performance of our previous prototype. To verify its practical capability, the optimized WB-TENG is employed to power several electronic devices, including a digital watch and 50 commercial LEDs, confirming its potential for real-world energy harvesting applications. This work presents a novel and effective strategy for harnessing wind energy under dynamic environmental conditions, offering a sustainable approach for decentralized energy collection in low-speed and omnidirectional wind settings. Full article

To address the challenges of adsorption instability and obstacle-crossing difficulties faced by wall-climbing robots in the harsh operation and maintenance (O&M) environment of offshore wind turbine (OWT) towers, this paper presents the design of a magnetic-adhesive wall-climbing robot with a planetary-gear configuration and investigates its wind resistance stability. First, the magnetic circuit layout is optimized through finite element analysis, revealing that the F-16 continuous planetary configuration (16 poles) effectively suppresses magnetic flux leakage and forms an integrated magnetic pad, maintaining adsorption force at a large air gap of 20 mm, thereby enhancing magnetic robustness during obstacle crossing and making it the optimal choice for high-load offshore conditions. Second, an unsteady flow field model based on the Kaimal turbulence spectrum is constructed to analyze aerodynamic loads. Fluid–structure interaction (FSI) simulations demonstrate that at a height of 30 m, the turbulence integral scale matches the robot dimensions, and combined with the Venturi effect of gap jet flow, this leads to peak turbulence intensity and pitching moment, creating a hazardous, pronounced aerodynamic amplification condition. Finally, an anti-slip stability model is established, revealing that vertical wall climbing represents the critical loading scenario; the magnetic adhesion system must deliver a total adsorption force of no less than 1000 N to resist a 35 m/s wind speed under low-friction conditions, providing a quantitative design basis for anti-wind safety. This study integrates magnetic circuit optimization, turbulence-resolved aerodynamics, and macroscopic anti-slip mechanics, offering theoretical support and engineering guidance for the safe deployment of intelligent O&M equipment for offshore wind power. Bench-scale measurements of magnetic adhesion force, friction coefficient, and translation force fluctuation support the exponential-decay magnetic model and the multi-wheel phase-interleaving concept; however, the current 4 × 16-pole prototype delivers ~627 N at the 2 mm working gap, below the 1000 N design target. The design methodology is therefore validated, while the current physical configuration requires further iteration of the working gap or magnet grade before it can be considered operationally adequate. Full article

Accurate wind power forecasting is essential for renewable-energy accommodation, low-carbon dispatch, and the sustainable operation of modern power systems. However, wind power series exhibit pronounced non-stationarity, strong volatility, and multi-scale evolution, making long-term trends and short-term disturbances difficult to characterize jointly. In addition, multi-step forecasting errors tend to accumulate with increasing horizons, degrading model accuracy and stability. To address these issues, this study proposes CoFFormer, a collaborative frequency-domain-enhanced network for non-stationary wind power forecasting. The model reduces input modeling complexity, strengthens collaborative representation of heterogeneous temporal information, and suppresses output-stage error accumulation. Specifically, embedded series decomposition mitigates coupling interference between trend and fluctuation components. Differentiated temporal modeling and dynamic gating then adaptively coordinate the contributions of different feature representations, while frequency-domain residual compensation enhances the recovery of periodic structures and local oscillations. Experiments on ETTh2, wind_speed, WindPower, and Location2 demonstrate strong competitiveness across forecasting horizons. CoFFormer achieves MSE/MAE values of 0.0957/0.2238 and 0.1508/0.2889 on ETTh2 for 12- and 24-step forecasting, and 0.0617/0.1490 and 0.3838/0.3948 on WindPower for 3- and 24-step forecasting, outperforming most baselines. Ablation studies confirm the effectiveness and synergy of each component, providing an effective solution for high-accuracy multi-step forecasting of complex non-stationary wind power series. Full article

This study is the first to assess airborne allergenic pollen in Erzincan, a continental climate region with mixed native vegetation, agricultural land, and urban areas, using volumetric sampling and comprehensive statistical models. Weekly samples from 2022 to 2023 were converted to 24 h slides and examined microscopically, and concentrations were calculated as pollen/m³ according to REA methodology. A total of 730 days of observations were evaluated. The annual pollen integrals were calculated separately for each year, and their combined total was 22,225 pollen*day/m³ (2022: 8667; 2023: 13,558). Woody pollen comprised 53.67% (2022) and 51.86% (2023); herbaceous pollen comprised 46.01% (2022) and 48.07% (2023). Thirty six pollen taxa were identified; the peak months were May 2022 (24.5%) and June 2023 (27.5%). Daily total pollen concentration was correlated positively with temperature (r_s = 0.33, p < 0.001) and weakly with wind speed (r_s = 0.17, p < 0.001) but negatively correlated with humidity (r_s = −0.26, p < 0.001). Precipitation showed no significant association with pollen levels (r_s = −0.02, p = 0.13), indicating that warmer and drier conditions favor higher pollen concentrations. The LMM showed that pollen density was affected by meteorological variables as well as interannual climatic variation, with temperature being the positive predictor (β = 0.348; p < 0.001). Canonical correlation analysis revealed a significant, multidimensional relationship between meteorological variables (temperature, humidity, rainfall, wind) and the daily densities of eight pollen taxa (Wilks’ λ = 0.603; p < 0.001). Canonical Correspondence Analysis indicated that meteorological variables influenced pollen taxon composition (F = 20.235, p = 0.001), explaining 11.51% of total inertia and suggesting that additional ecological factors may also contribute. In conclusion, pollen dynamics are sensitive not only to seasonal and meteorological variables but also to interannual climate fluctuations; in particular, hot, dry periods prolong the persistence of airborne pollen, while short-term rainfall can trigger sudden pollen releases in some species. Full article

With the increasing penetration of renewable energy and the intensification of load fluctuations, power electrical equipment frequently operates under complex conditions such as heavy loads. Traditional spinning reserve allocation methods based on fixed outage rates are inadequate for accurately characterizing the dynamic operational risks of the system. To address this issue, this paper proposes an optimal spinning reserve allocation method for power systems considering operational reliability. First, by introducing the condition-dependent outage replacement rate (CDORR), a reliability model under multi-uncertainties is established, which considers load forecast errors, wind power randomness, and equipment operating conditions. Second, to overcome the “curse of dimensionality” caused by alternating iterative solving in complex scenarios, analytical expected energy not served (EENS) formulations based on the sensitivity method are derived to rapidly quantify the power flow variations between pre-contingency and post-contingency states. Finally, aimed at the non-convexity introduced by bilinear terms in the model, an improved segmented McCormick envelope relaxation algorithm is designed, which is combined with the Tangent Plane Cut Collection (TPCC) strategy to effectively eliminate redundant envelope regions. Case studies based on the IEEE 30-bus system demonstrate that, while ensuring evaluation accuracy, the proposed method accelerates computational speed by nearly 20 times compared to traditional nonlinear solvers. Moreover, it effectively mitigates potential operational risks under complex conditions, achieving an optimal balance between system reliability and economics. Full article

In response to stochastic fluctuations in large-scale wind power integration and the resulting peak-shaving challenges, high-precision forecasting with physical consistency is essential for grid safety. To address the inefficiency of physical models and poor interpretability of data-driven methods, this paper proposes a hybrid FCM-WGM-BiLSTM-Transformer (FW-BTP) framework integrating Fuzzy C-Means (FCM) clustering, Weighted Grey Model (WGM) trend extraction, and a coupled BiLSTM-Transformer module. Physical prior knowledge—wind turbine power curves—is embedded into the loss function via a Physics-Guided Loss Regularization (PGL) mechanism. Validated on measured data from a Xinjiang wind farm, the model achieves an $R^2$ of 0.9967, MAE of 6.11, and RMSE of 11.19. The proposed model reduces $R^2$ by 37% compared to the newer model KAN, and compared to the better-performing recurrent baseline model (BiLSTM, MAE = 8.75 MW), the proposed FW-BTP model reduces the MAE by 30% (to 6.12 MW). Ablation studies confirm the WGM reduces LogCosh loss from 9.57 to 5.12, and SHAP analysis verifies sensitivity to trend and physical wind speed features. The framework balances accuracy, robustness, and interpretability, supporting refined scheduling in modern power systems. Full article

This study employs a multi-region coupled method to investigate the motion responses and aerodynamic load variations of the IEA 15 MW semi-submersible floating wind turbine (FOWT) under extreme focused wave conditions. The methodology employs the self-developed MRFoam solver within OpenFOAM to integrate aerodynamic and hydrodynamic analyses. The computational framework combines an incompressible viscous flow model with kOmegaSST turbulence closure and an actuator line representation of turbine blades. Extreme wave conditions are generated using NewWave theory, with systematic variations in wave height and wind speed to evaluate coupled effects. Results demonstrate that platform heave responds predominantly to wave excitation, showing minimal wind sensitivity. Turbine thrust maintains consistent mean values across wave conditions but exhibits wind-speed-dependent fluctuations. Mooring dynamics correlate strongly with surge motions, showing amplified tension variations from wave-induced platform displacements, though mean tensions remain stable under uniform wind regardless of wave magnitude. Full article

Asphalt bridge deck pavements are highly susceptible to rutting, shoving, and interlayer slippage under high-temperature traffic conditions, where interlayer shear stress plays a decisive role. To clarify the coupled effects of thermal gradients and moving loads, this study developed a sequential three-dimensional thermo-mechanical finite element model for a double-layer pavement in Zhongshan, China. Field-recorded air temperature, solar radiation, sunshine duration, and wind speed were used to define transient thermal boundaries. The calculated temperature field was then transferred to a fully bonded moving-load model with dual rectangular contact areas and braking-induced longitudinal traction. Axle load, roadway slope, braking coefficient, and the thicknesses of the SMA-13 and AC-20 layers were varied. The predicted temperature fluctuation attenuated and the peak time was delayed with depth. The pavement surface reached 58.95 °C at 13:00, whereas the bottom of the asphalt overlay reached 46.99 °C at 17:00. Under the adopted 14:00 near-peak summer condition, increasing axle load amplified the overall response and raised the maximum asphalt-layer shear response from 0.172 to 0.223 Mpa. Roadway slope mainly affected traffic-direction stress transfer. Increasing the braking coefficient from 0 to 0.7 increased longitudinal shear response from 57.9 to 161.2 kPa in the asphalt layers and from 56.4 to 112.6 kPa near the AC-20/concrete interface. Increasing SMA-13 thickness reduced thermal and mechanical demand in the underlying layers, whereas increasing AC-20 thickness reduced the response near the concrete deck but shifted part of the tensile and shear demand toward the upper asphalt layer. Full article

The blades directly affect the safety and power generation efficiency of the wind turbines. With the blade size increases, the reliable modal identification becomes important for vibration-based health monitoring. Although operational modal analysis (OMA) technique has been used in condition monitoring for the wind turbine blades, most existing studies focus on investigating a specific single method or under ideal excitation conditions. To overcome this limitation, this study takes the IEA-15MW large wind turbine blade as the research object and compares three OMA methods through numerical simulations, namely covariance-driven stochastic subspace identification (SSI-COV), frequency domain decomposition (FDD), and poly-reference least squares complex frequency domain (PolyMAX). The performance of the modal parameter identification methods is evaluated with respect to different sensor layouts, blade–tower coupling conditions, and environmental excitations. The results indicate that sparse sensor deployment cannot reliably identify the damage-sensitive high-order and complex modes. A nine-channel layout concentrated near second-order deformation regions significantly improves the identification of second-order flapwise frequencies and controls the average error of the first six modes within 3%. PolyMAX shows the best identification stability under different numbers and layouts of the sensors. Blade–tower coupling changes the blade modal characteristics and increases identification difficulty. Under this condition, FDD can still identify both low-order and high-order modes with good stability. Under different real wind conditions, the increasing wind speed causes the aerodynamic load to deviate from the white noise assumption, generally leading to fluctuations in the identification errors, with relatively large local errors occurring at certain medium and high wind speeds. Overall, the three OMA methods show different advantages under different identification conditions. PolyMAX shows the best stability under different sensor layouts and performs best when wind speed increases in the coupled wind turbine model, indicating that it is the most suitable for the actual complex coupling effects and environmental conditions. This research hopefully provides a basis for the subsequent engineering application of vibration-based modal identification of large offshore blades. Full article

With the increasing penetration of renewable energy, smoothing wind-farm-level power fluctuations at the point of common coupling has become an important requirement for maintaining grid stability. However, the dynamic power-tracking mechanism of large-inertia slope-track solid gravity energy storage systems remains insufficiently understood. This paper presents a theoretical and control-oriented study of a slope-track solid gravity energy storage system by establishing a low-speed-branch electromechanical coupling model, deriving its steady-state power-speed characteristics, and formulating several feedforward-feedback electromagnetic torque control laws. Pure feedforward control, power feedback, speed feedback, and a gain-scheduled speed feedback law are analyzed within a unified framework. The results show that, for a given power command and under sufficient torque-control authority, increasing the moving load or slope angle enhances the gravity-driven torque and reduces the required operating speed, acceleration, and cumulative displacement. This mechanism indicates that systems with heavier loads or steeper slopes have an advantage in smoothly responding to wind-power fluctuations at a fixed power scale. Frequency-response analysis further shows that the low-speed branch is suitable for slow and medium time-scale power smoothing, whereas rapidly varying commands remain constrained by the closed-loop response time and inertial correction terms. To demonstrate a finite-track implementation of the theory, a single-track multi-unit scheme with 30 standard load units is designed and numerically tested for wind-farm-side smoothing and slow-varying command tracking. This analysis provides a theoretical basis for prototype design and parameter selection before costly full-scale experimental validation. Full article

Stator winding reconfiguration technology has attracted increasing attention because it can modify the torque–speed output characteristics of motors online and extend the high-efficiency operating region. However, conventional winding switching methods suffer from several limitations, including long transient duration, typically at the millisecond level; difficulty in precise control; current interruption or asymmetric operation during the transient process; and the resulting torque loss and torque fluctuation. To address these issues, this paper first proposes a seamless winding switching method. The core idea is to complete the switching within the zero-vector interval of a single SVPWM period, thereby eliminating potential surge voltage without requiring a snubber circuit. The proposed method reduces the transient duration to only 5 μs, eliminates current interruption and current asymmetry, and suppresses the associated torque ripple. Furthermore, a deadbeat predictive control strategy is employed. By directly controlling the voltage vector, the proposed strategy enables fast and accurate current tracking within one sampling period after reconfiguration, thereby avoiding current overshoot, oscillation, and the corresponding torque and speed fluctuations. In addition, a voltage feedforward compensation method is proposed to pre-compensate the voltage applied during the PWM period in which the winding reconfiguration is performed, thereby preventing the application of voltage vectors that are inconsistent with the actual motor winding connection. With these three innovative measures, fast, smooth, and robust winding reconfiguration is achieved. The mechanisms and implementation procedures of the proposed strategies are described in detail, and their feasibility and effectiveness are verified through experiments. Full article

Accurate ultra-short-term wind power forecasting is of great significance for grid integration scheduling and the secure operation of power systems. However, due to meteorological disturbances and turbine operating states, wind power series generally exhibit non-stationary, multi-scale fluctuations and strong nonlinearity. To improve forecasting accuracy, this paper proposes an ultra-short-term wind power forecasting model based on a two-stage signal decomposition and a hybrid architecture combining iTransformer, LSTM, and KAN. First, a cascaded decomposition module is constructed using the wavelet transform (WT) and ICEEMDAN to attenuate the non-stationarity of the original power series and to extract multi-scale features. An iTransformer branch is then employed to model global dependencies among multiple variables, while an LSTM branch captures temporal dynamics in the historical power series. Subsequently, a cross-attention mechanism is introduced to achieve cross-branch feature fusion, and a KAN output layer is adopted to enhance the model’s representation of the wind speed–power nonlinear mapping. A particle swarm optimization (PSO) algorithm, combined with a cosine annealing strategy, is used to optimize key hyperparameters and improve training stability. Experimental results using SCADA data from a 150 MW wind farm in southern Hunan Province show that the proposed model achieves an MAE of 9.8327 MW, an RMSE of 13.1872 MW, an SMAPE of 18.8474%, and an $R^2$ of 0.7798. These values correspond to the fixed main comparison protocol used for baseline evaluation, while the ablation study reports multi-seed mean and standard deviation results to assess module-level robustness. Compared with LSTM and WT-ICEEMDAN-CNN-LSTM, the proposed model achieves clear improvements in forecasting accuracy and fitting capability. Additional cross-wind-farm validation on a second wind farm shows that WT-ICEEMDAN-iTransformer-LSTM-KAN-PSO (hereafter referred to as ILKP) maintains the best overall performance, achieving an MAE of 27.2193 MW, an RMSE of 36.1862 MW, an SMAPE of 27.8429%, and an $R^2$ of 0.5189, demonstrating transferability and robustness under different operating conditions. Full article

The increasing proportion of wind power integration in the power systems and the dynamic interaction of power electronic equipment lead to frequent subsynchronous oscillation, which seriously threatens the safety and stability of the system. Therefore, it is urgent to develop a high-precision and robust identification method. Traditional standalone identification methods are vulnerable to wind speed fluctuations and noise, resulting in unsatisfactory accuracy and robustness. To accurately extract the oscillation parameters from the active power signal, this paper proposes a hybrid method for identifying subsynchronous oscillation in power systems. First, the active power signal is preprocessed using the wavelet threshold denoising strategy, which effectively filters out noise through multi-scale decomposition and signal reconstruction. Second, VMD is applied to the preprocessed signal to decompose it into intrinsic mode functions, thereby achieving effective separation of different oscillatory characteristics. Finally, the fast Fourier transform is used to perform spectral analysis on each IMF to accurately capture the dominant frequency and amplitude of each oscillating component. On the four-machine two-area system with DFIG, the verification is carried out under the wind speeds of 9 m/s, 10.5 m/s and 12 m/s and the no noise, 60 dB and 40 dB noise conditions. SSO is excited by inserting 20 Hz, 30.5 Hz or 40 Hz subsynchronous oscillation components into the external part of the mechanical power input of the generator. The results show that the relative error of frequency identification of the proposed method is less than 0.0426% under all test conditions. The relative error of amplitude identification is less than 1.4155%. Compared with different methods, the proposed method exhibits excellent performance under noise conditions and has robustness to wind change and noise interference. Full article

To address the challenge of achieving a unified dynamic evaluation of in-service performance for reflector antennas subjected to coupled wind disturbances, structural flexibility, and servo control, an end-to-end performance evaluation method based on integrated modeling is proposed. A disturbance–structure–electromagnetic–control integrated modeling framework is constructed, in which the fluctuating wind load, structural dynamics model, cascaded servo control, and end-to-end performance mapping model are unified within a state-space closed-loop system, thereby enabling time-domain dynamic evaluation from environmental excitation inputs to performance index outputs. The Davenport spectrum and harmonic superposition method are adopted to establish a stochastic fluctuating wind model, and structural disturbance inputs are formed through wind pressure linearisation and modal projection. A low-order flexible dynamic model of the reflector antenna is developed using finite element modal condensation, and a main-axis closed-loop control model is formulated by incorporating fuzzy active disturbance rejection control and notch filtering. By combining the best-fit parabolic surface, the weighted half-path-length difference, and the Ruze formula, an end-to-end mapping model that relates structural nodal displacements to electromagnetic performance degradation is established. The research demonstrates that the proposed method can effectively reveal the influence of wind speed, elevation angle, and flexible mode coupling on antenna performance. Furthermore, a performance evaluation system developed based on this method integrates parameter input, simulation computation, and result output, providing an effective tool for antenna design optimization and performance assurance. Full article

Ground-level ozone (O₃) has become a major air pollutant in China following PM_2.5, particularly in the southeastern coastal region, where the frequent interaction of typhoons and the subtropical high complicates pollution control. In this paper, spatial autocorrelation and a multiscale geographically weighted regression (MGWR) model were employed to estimate the spatiotemporal heterogeneity and driving mechanisms of O₃ in the Southeast Coastal urban agglomerations from 2015 to 2024. Temporally, the annual average O₃ concentration exhibited a fluctuating trend of an initial increase, followed by a decrease and a subsequent rebound. A bimodal monthly pattern was observed, with peaks in May–June and August–September and minima in winter. Diurnally, the concentration showed a consistent pattern of being higher in the daytime and lower at night, peaking in the afternoon, driven by solar radiation and temperature. Spatially, O₃ exhibited a distinct north–south gradient, with the highest in Jiangsu Province, followed by Shanghai, Zhejiang and Guangdong, and the lowest in Fujian. Significant spatial autocorrelation was detected, with hot spots in the Yangtze River Delta and cold spots in Fujian and adjacent areas. Seasonally, the most severe pollution with the greatest spatial heterogeneity, occurred in summer, contrasting with the uniformly low concentrations in winter. Compared with OLS and GWR, the MGWR demonstrated superior explanatory power. O₃ was jointly influenced by precursors, natural factors, and socioeconomic factors, with the influence intensity ranked as follows: NO₂ > average elevation > population density > annual precipitation> wind speed > built-up area > proportion of the secondary industry in GDP. Notably, the effects of NO₂, annual precipitation, and the proportion of the secondary industry exhibited strong spatial heterogeneity, operating at finer spatial scales. These findings provide scientific support for sustainable air quality management and region-specific O₃ control in southeastern coastal China. Full article