Search Results (279)

Although interest in tidal energy has increased in recent years, its development remains significantly behind that of other renewable sources such as solar and wind energy. This delay is primarily caused by the complex and harsh ocean environment, which imposes significant constraints on operational systems. This paper proposes a new approach to the design and control of a marine current turbine (MCT) emulator without a pitch mechanism, operating in real time below the rated marine current speed.The emulator control strategy integrates two approaches: predictive control for regulating the speed of the DC machine, and a Linear Quadratic Regulator (LQR) control scheme for maximizing power extraction from the marine current. Our experimental results demonstrate the effectiveness of the proposed hybrid control strategy, which allows precise tracking of reference signals and stable regulation of the direct current machine (DCM) speed, thereby ensuring synchronization with the turbine’s rotational speed. This approach ensures optimal and robust performance over the entire range of marine current variations. Full article

In recent years, the installed capacity of renewable energy sources, such as wind power and photovoltaic generation, has been steadily increasing in power systems. However, the inherent randomness and volatility of renewable energy generation pose greater challenges to grid frequency stability. To address this issue, this paper first introduces the Minkowski sum algorithm to map the feasible regions of dispersed individual units into a high-dimensional hypercube space, achieving efficient aggregation of large-scale schedulable capacity. Compared with conventional geometric or convex-hull aggregation methods, the proposed approach better captures spatio-temporal coupling characteristics and reduces computational complexity while preserving accuracy. Subsequently, aiming at the coordination challenge between day-ahead planning and real-time dispatch, a “hierarchical coordination and dynamic optimization” control framework is proposed. This three-layer architecture, comprising “day-ahead pre-dispatch, intraday rolling optimization, and terminal execution,” combined with PID feedback correction technology, stabilizes the output deviation within ±15%. This performance is significantly superior to the market assessment threshold. The research results provide theoretical support and practical reference for the engineering promotion of vehicle–grid interaction technology and the construction of new power systems. Full article

Accurate estimation of Total Phosphorus, referred to as “Phosphorus, Total” (PPUT; µg/L) in the sourced monitoring data, is essential for understanding eutrophication dynamics and guiding water-quality management in inland lakes. However, lake-wide PPUT mapping at high resolution is challenging to achieve using conventional in-situ sampling, and nearshore gradients are often poorly resolved by medium- or low-resolution satellite sensors. This study exploits multi-generation PlanetScope imagery (Dove Classic, Dove-R, and SuperDove; 3–5 m, near-daily revisit) to develop a hybrid AI framework for PPUT retrieval in Lake Simcoe, Ontario, Canada. PlanetScope surface reflectance, short-term meteorological descriptors (3 to 7-day aggregates of air temperature, wind speed, precipitation, and sea-level pressure), and in-situ Secchi depth (SSD) were used to train five ensemble-learning models (HistGradientBoosting, CatBoost, RandomForest, ExtraTrees, and GradientBoosting) across eight feature-group regimes that progressively extend from bands-only, to combinations with spectral indices and day-of-year (DOY), and finally to SSD-inclusive full-feature configurations. The inclusion of SSD led to a strong and systematic performance gain, with mean R² increasing from about 0.67 (SSD-free) to 0.94 (SSD-aware), confirming that vertically integrated optical clarity is the dominant constraint on PPUT retrieval and cannot be reconstructed from surface reflectance alone. To enable scalable SSD-free monitoring, a knowledge-distillation strategy was implemented in which an SSD-aware teacher transfers its learned representation to a student using only satellite and meteorological inputs. The optimal student model, based on a compact subset of 40 predictors, achieved R² = 0.83, RMSE = 9.82 µg/L, and MAE = 5.41 µg/L, retaining approximately 88% of the teacher’s explanatory power. Application of the student model to PlanetScope scenes from 2020 to 2025 produces meter-scale PPUT maps; a 26 July 2024 case study shows that >97% of the lake surface remains below 10 µg/L, while rare (<1%) but coherent hotspots above 20 µg/L align with tributary mouths and narrow channels. The results demonstrate that combining commercial high-resolution imagery with physics-informed feature engineering and knowledge transfer enables scalable and operationally relevant monitoring of lake phosphorus dynamics. These high-resolution PPUT maps enable lake managers to identify nearshore nutrient hotspots, tributary plume structures. In doing so, the proposed framework supports targeted field sampling, early warning for eutrophication events, and more robust, lake-wide nutrient budgeting. Full article

Supply–demand balance in wind–solar dominant energy transition is challenged by the volatility of wind–solar power. Complementarity of wind–solar power has been introduced to suppress this volatility. Although multiple indices have been developed to quantify complementarity, a quantitative index with explicit physical meaning remains lacking. Additionally, complementarity’s temporal stability, which is imperative for wind–solar site selection, is unclear. In this study, these knowledge gaps are closed through developing a Daily Complementarity Index of wind–solar generation (DCI) and a nuanced national assessment of complementarity in China. The results of the comparison of our index with existing indices and site validation confirm the reasonability of the DCI and its improvements in interpretability. The average DCI of China ranges from 0.06 to 0.88, with a pronounced low-DCI zone across the Sichuan Basin and Chongqing municipality, and a high–DCI zone along the Three-North Shelterbelt. Temporally, the complementarity of wind–solar power in China follows a slight increase trend (3.96 × 10⁻⁵ year⁻¹), with evident seasonal characteristics, in which the highest and lowest are 0.37 and 0.17, respectively. This study introduces an effective tool for quantifying complementarity, and these findings can offer valuable reference for China’s renewable energy transition. Full article

Ocean winds and waves play a vital role in maritime navigation safety, offshore operations, and coastal zone dynamics. Although both factors have been widely studied individually, the joint characterization of wind and wave events remains limited in the North Indian Ocean. This study, utilizing ERA5 reanalysis data from 1980 to 2022, statistically analyzed the distribution and variation patterns of both wind speed and significant wave height, investigating the occurrence, affected area proportion, frequency, and intensity of SBLWEs. To understand the cause of Strong Breeze and Large Wave Events (SBLWEs), their connections with other phenomena, such as tropical cyclones, were also explored. The results show that regions with strong breezes and large waves are mainly concentrated in the central and western Arabian Sea near Africa and the central and western Bay of Bengal. Monthly averages indicate that wind and wave intensity are much higher during the summer monsoon than in other seasons, with high intensity, probability, and extensive affected areas of SBLWEs. The occurrence probability of SBLWEs is highest in the central and western Arabian Sea (up to ~40%), and the highest probability in the Bay of Bengal is about 20% near the eastern coast of Sri Lanka. The peak period of SBLWEs occurs from June to August, with the largest affected area in July, reaching almost 25%. Over the past 40 years, the number of SBLWEs has shown an increasing trend, with an average of 0.7 events annually. The intensity distribution of SBLWEs resembles that of wind speed and wave height, with the highest intensity areas concentrated in the Bay of Bengal, affected by tropical cyclones. This study can serve as a scientific reference for maritime route planning and offshore operations, helping to reduce the negative impacts of large wind and wave events and enhance navigation safety. Full article

The representativeness of long-term wind data at a site remains a challenge, as it is essential for resource analysis, production adjustment in operating plants, and the simulation of hybridised plants. A representative one-year hourly time series, known as a Wind Reference Year (WRY), is required, yet the availability of long-term real data is rare, making the estimation of WRY from reanalysis data and shorter measurement campaigns a common approach. In this study, Gaussian Mixture Copula Models (GMCM) and five regression models were applied and compared. The GMCM was trained using 15 years of reanalysis data to generate simulations, and subsequently, regression-based Measure–Correlate–Predict (MCP) methods were applied to adapt the simulated reference year to site-specific conditions. Finally, the Hungarian algorithm was used to reorder the simulated data series, aligning it with a typical wind pattern and producing the WRY dataset. The results were validated against 15 years of real measurements and benchmarked against a heuristic method based on long-term similarity of main wind parameters and the commercial tool Windographer. The findings demonstrate the potential of the proposed method, showing improvements over existing techniques and providing a robust approach to constructing representative WRY datasets. Full article

Natural hazards exert a detrimental influence on human survival, environmental conditions and society. Historical hazard events have generated a broad corpus of literature addressing the spatiotemporal extent, dissemination or social responses. With regard to quantitative analysis based on information locked within verbose text, the release of such information from the narrative format is encouraging. Natural Language Processing (NLP), a technique demonstrated to be capable of automated data extraction, provides a useful tool in establishing a structured dataset on hazard occurrences. In our study, we utilize scattered textual records of historical natural hazard events to create a novel dataset and explore the applicability of NLP in parallel. We put forward a standard list of toponyms based on manual annotation of a compilation of disaster-related texts, all of which were references in an authoritative publication in the field. The final natural hazards dataset comprised location data, which referred to a specific hazard report in Europe during 1301–1500, together with its geocoding result, year of occurrence and detailed event(s). We evaluated the performance of four pre-trained geoparsing tools (Flair, Stanford CoreNLP, spaCy and Irchel Geoparser) for automated toponym extraction in comparion with the standard list. All four tested methods showed a high precision (above 0.99). Flair had the best overall performance (F1 score 0.89), followed by Stanford CoreNLP (F1 score 0.83) and Irchel Geoparser (F1 score 0.82), while spaCy had a poor recall (0.5). Then we divided natural hazards into six categories: extreme heat, snow and ice, wind and hails, rainstorms and floods, droughts, and earthquakes. Finally, we compared our newly digitized natural hazard dataset to a geocoded version of the dataset provided by Harvard University, thus providing a comprehensive overview of the spatial–temporal characteristics of European hazard observations. The statistical outcomes of the present investigation demonstrate the efficacy of NLP techniques in text information extraction and hazard dataset generation, offering references for collaborative and interdisciplinary efforts. Full article

Tree pollen is the most abundant in the Community of Madrid (CAM), and specifically, pollen types from Olea, Cupressaceae, and Platanus are the most allergenic, after Gramineae, in this Spanish region. Air pollutants are one of the most significant stress factors for wind-pollinated vegetation, especially in urban areas, and can cause alterations in the immune system and the consequent triggering of type I hypersensitivity reactions mediated by immunoglobulin E (IgE). This study analyses the allergic sensitization caused by the interrelation of O₃, NO₂, and PM₁₀ pollutants with the tree pollen types Olea, Cupressaceae, and Platanus in the period 2017–2021. To this end, general linear models were calculated using the Statgraphics Centurion 19 tool. The data collected came from the Air Quality Networks of the CAM and Madrid City Council, the CAM Palynological Network, and the Allergy Services of the reference hospitals in the five study areas. This research confirms a statistically significant correlation between allergic sensitivity to pollen types and their concentrations in the air, and those of atmospheric pollutants, in the different areas and years studied. These pollen and pollutant concentrations in the atmosphere of the CAM jointly influence the prevalence of allergic sensitisation, as is evident in all the models calculated. Full article

The operational phase of offshore wind farms, lasting up to 20–25 years, exceeds the construction phase in duration. The ecological effects of underwater noise demand serious consideration, necessitating urgent research into its acoustic characteristics. This review conducts a systematic analysis of measurements of underwater noise from operational offshore wind farms, considering the correlations between turbine noise and distance, wind speed, turbine power, and foundation type. Propagation distance is the most critical factor influencing the underwater sound pressure level (SPL) of wind turbines, exhibiting a negative correlation with the SPL, with an attenuation of approximately 20.4 dB/decade. In contrast, wind speed and turbine power show a positive correlation with the SPL, with increase rates of 18.5 dB/decade and 12.4 dB/decade, respectively. Further analysis shows that foundation type and drive technology also have a significant impact on underwater SPL. With technological innovation, specifically the upgrade from conventional geared drive to direct-drive technology, the level of underwater noise can be reduced by approximately 9 dB, with the primary peak frequency being shifted to a lower range. Moreover, significant variations in SPLs were noted with the utilization of various types of foundation structures, with monopile foundations exhibiting the highest SPLs of underwater noise. These conclusions have important reference value for the scientific assessment of the health of aquatic organisms and ecosystems. Full article

The standing seam metal roof system is wind-sensitive due to its light weight and decreasing stiffness as the span increases, and in recent years there have been a number of wind-exposed damages to the structures where these roof systems have been applied. In order to study the wind-uplifted resistance reliability of different types of standing seam metal roof systems, and then to evaluate their safety level, a reliability analysis framework was developed. The proposed approach integrates the Latin Hypercube Sampling–Monte Carlo Simulation (LHS–MCS) method to assess the wind-uplifted resistance reliability of standing seam metal roof systems. Taking Jinan Yaoqiang International Airport Terminal Building’s standing seam Al-Mg-Mn roof system and Urumqi Tianshan International Airport Transportation Center’s standing seam Al-Zn-plated steel roof system as the objects of research, the research was carried out from the aspects of wind uplift test, wind tunnel test, finite element simulation, and wind-uplifted resistance reliability analysis. The study shows the following: the wind-uplifted resistance bearing capacity of the roof systems is significantly affected by the width of the roof panel, the spacing of the fixed support, the thickness of the roof panel, and the diameter of end interlocking; the effects of the differences in structural parameters and roof types are eliminated by the introduction of a damage index, and the failure forms of different types of roof systems can be unified, and the corresponding limit state function can then be deduced; based on the LHS–MCS method, the reliability indexes of the two common types of standing seam metal roof systems were obtained to be 3.0975 and 3.2850, respectively, which are lower than the requirements of the code for the first safety level, and it is recommended that reinforcement measures be prioritized at the connection points between roof panel and support, such as reducing the spacing of the fixed support or decreasing the diameter of end interlocking, to improve the structural safety. The above study can provide a reference for the safety level assessment, wind resistant design, and sustainable operation and maintenance of different types of standing seam metal roof systems. Full article

Efficient wind power forecasting is critical in achieving large-scale integration of wind energy in modern electricity systems. On the other hand, limited availability of wealthy, long-term historical data of wind power generation for many sites of interest often challenges the training of tailored forecasting models, which, in turn, introduces uncertainty concerning the anticipated operational status of similar early-life, or even prospective, wind farm projects. To that end, this study puts forward a spatiotemporal, national-level forecasting exercise as a means of addressing wind power data scarcity in Greece. It does so by developing a hybrid wavelet-enhanced deep learning model that leverages long-term historical data from a reference site located in central Greece. The model is optimized for 24-h day-ahead forecasting, using a hybrid architecture that incorporates discrete wavelet transform for feature extraction, with deep neural networks for spatiotemporal learning. Accordingly, the model’s generalization is evaluated across a number of geographically distributed sites of different quality wind potential, each constrained to only one year of available data. The analysis compares forecasting performance between the original and target sites to assess spatiotemporal robustness of the model without site-specific retraining. Our results demonstrate that the developed model maintains competitive accuracy across data-scarce locations for the first 12 h of the day-ahead forecasting horizon, designating, at the same time, distinct performance patterns, dependent on the geographical and wind potential quality dimensions of the examined areas. Overall, this work underscores the feasibility of leveraging data-rich regions to inform forecasting in under-instrumented areas and contributes to the broader discourse on spatial generalization in renewable energy modeling and planning. Full article

This study focuses on quantifying wind–temperature load combination coefficients for long-span hybrid cable-stayed suspension bridges (HCSSBs) to overcome limitations of traditional methods in ignoring load correlation and geometry nonlinearity. A probabilistic framework is proposed to use site-specific load data to determine load combination coefficients, focusing on load correlation and geometric nonlinearity while assuming that stress reflects load effects and that 100-year samples are statistically representative. Long-sequence meteorological data, including wind and temperature measurements, were used to construct marginal and bivariate joint distributions, which characterize the randomness and correlation of wind and temperature loads. Load samples covering the design reference period were generated and validated via convergence tests. Four load scenarios (individual temperature, individual wind, linear superposition, and nonlinear coupling) were designed, and key control points are screened using indicators reflecting the comprehensive load effect $\stackrel{-}{\mathit{EII}}$, combined load proportion $\zeta$, and nonlinear influence $\eta$. Based on stress responses of key control points, load combination coefficients were derived with probability modeling. A case study for a bridge with span length of 2300 m shows that the load combination coefficients for the main girder are 0.60 (east wind) and 0.59 (west wind), while they are 0.51 (east wind) and 0.58 (west wind) for the main tower. These results demonstrate that the proposed method enables the provision of rational load combination coefficients. Full article

To address the complex operating conditions and challenging dynamic characteristics of bearings in the main shaft transmission system of wind turbines, this study investigates a specific wind turbine model. By comprehensively considering factors such as main shaft structure, cumulative damage, and stochastic wind loads, we adopt a modular analysis framework integrating the wind field, aerodynamics, the structural response, and fatigue life prediction to establish a method for predicting the fatigue life of main shaft bearings under stochastic wind conditions. To verify this method, the fixed-end main shaft bearing of a 4.5 MW wind turbine was selected as a case study. The research results show the following: (1) Increases in both average wind speed and turbulence intensity significantly shorten the fatigue life of the bearing. (2) Higher turbulence intensity amplifies the dispersion of the speed and load of rolling elements, thereby increasing the probability of extreme operating conditions and exerting an adverse impact on fatigue life. (3) The average wind speed has a significant influence on the overall fatigue life: within a specific range, the fatigue failure probability of the main bearing increases as the average wind speed decreases. (4) The impact of wind speed fluctuations on the hub center load is much greater than that caused by rotational speed changes. (5) In addition, the modular design method adopted in this study calculates that the fatigue life of the fixed-end bearing is 28.8 years, with an overall error of only 0.8 years compared to the 29.6-year fatigue life obtained using Romax simulation software. This research provides important theoretical references and engineering value for improving the operational reliability of wind turbines and enhancing the accuracy of bearing fatigue life prediction. Full article

The World Meteorological Organization/Global Atmosphere Watch (WMO/GAW) observation site of Lamezia Terme (code: LMT) in Calabria, Italy, has been measuring nitric oxide (NO) and nitrogen dioxide (NO₂) (together referred to as NO_x) for a decade; however, only a limited number of studies have evaluated their variability at the site, accounting for short measurement periods. In this work, nine continuous years (2015–2023) of measurements are analyzed to assess daily, weekly, seasonal, and multi-year tendencies, also accounting for local wind circulation, which is known to have a relevant impact on LMT’s measurements. For the first time, a multi-year evaluation of LMT data also considers the local wind lidar record to integrate conventional measurements with additional information on the transport of NO_x at low altitudes. The study also considers data on local tourism and vehicular traffic to assess correlations with LMT’s measurements, thus providing new insights on NO_x variability at the site. The analysis showed peaks in early morning NO_x concentrations attributable to rush hour traffic, while in the evening NO₂ peaks are present with minor NO counterparts. Weekly cycles have yielded the most statistically significant results of any other similar evaluation at the sites, with all combinations of parameters, seasons, and wind corridors indicating tangible differences between weekday (WD, Monday to Friday) and weekend (WE, Saturday and Sunday) concentrations. The analysis of multi-year variability has shown a slightly declining tendency; however, sporadic bursts in concentrations limit the statistical significance of downward trends. Full article

Evapotranspiration (ET) variation is typically influenced by climatic factors, which are considered the primary drivers of agricultural water requirements. Any changes in ET rates directly affect crop water demands. In this study, temporal trends and magnitudes of key climatic variables, and their impacts on reference evapotranspiration (ETo) during 1981–2020, were evaluated across 36 districts of Punjab, Pakistan. Positive serial correlations, ranging from 0.29 to 0.48, were identified and removed using the pre-whitening technique. Increasing trends in maximum temperature (Tmax) and wind speed (WS) across Punjab and its subregions were observed, while relative humidity (RH) exhibited both increasing and decreasing trends. No significant trends were detected for the minimum temperature (Tmin). On a monthly scale, in the Southern Punjab (SP) region, Sen’s slope estimated an increase in ETo, ranging from 0.239 mm/year in November to 0.636 mm/year in May, at a significance level of α = 0.05 (5%). At the provincial scale, significant upward trends in ETo were observed for the annual, Kharif, and autumn seasons, with Z-values of 2.04, 2.16, and 3.13, respectively, at α = 0.05 and 0.01. It was determined that, on an annual scale in Punjab, ETo sensitivity to climatic parameters followed the following order: Tmax > wind speed (WS) > Tmin > RH. The best-fitted models for Tmax, Tmin, WS, and RH were Gaussian, exponential, and spherical. ETo was found to increase spatially from North to South Punjab, with an approximate rise of 70–80 mm/decade. The results provide a scientific basis for understanding hydroclimatic drivers of ETo in semi-arid regions and contribute to improving climate impact assessments on agricultural water use. The observed ETo increases, particularly in South Punjab and lower Central Punjab, highlight the need for region-specific irrigation scheduling and water allocation. These findings can guide cropping calendars, improve irrigation efficiency, and increase canal water supplies to high-ETo areas, supporting adaptive strategies against climate variability in Punjab. Full article