Wind Speed Research Articles

Characteristics and Source Apportionment of Atmospheric Volatile Organic Compounds in Zhengzhou During O3 Campaign Period

An online gas chromatograph （GC5000） was used to monitor the volatile organic compounds （VOCs） in the atmospheric environment of Zhengzhou City during the ozone campaign period from May to September of 2022. The relationship between O3 and its precursors as well as meteorology was analyzed and the pollution characteristics of VOCs during the O3 exceeding and non-exceeding the standard days were compared and explored. Different VOC activity evaluation methods of OFP and L·OH were utilized to compare and analyze the key active components and species and the ratio analysis （RA） and positive matrix factorization （PMF） analysis models were used to study the apportionment contribution of VOCs. The results showed that the O3 pollution in June and September in Zhengzhou was mainly due to the adverse meteorological conditions of high temperature and low humidity, strong radiation, and low wind speed, superimposed by the prominent concentrations of local VOCs and NO2, resulting in frequently high and excessive O3 occurrences. The VOCs concentration in Zhengzhou during the campaign period was an average of （68.3 ± 18.4） μg·m-3, whereas it was 75.7 μg·m-3 during O3 exceeding standard days and 13.4 μg·m-3 during O3 non-exceeding days, respectively. Among the VOC species, the OVOCs was 31.6%, accounting for the highest mass fraction, followed by halogenated hydrocarbon, alkane, and aromatic hydrocarbon, and the major species were ethane, n-butane, dichloromethane, propane, isopentane, toluene, chloromethane, 1,2-dichloroethane, and acetylene. VOC diurnal variation indicated that the emission of VOC pollution sources in the morning, evening peak, and at night should be paid more attention. The contribution of VOCs to OFP during the campaign period was （130.5 ± 46.4） μg·m-3, and the L·OH was （6.5 ± 2.9） s-1, among which the top 15 species with high activity were primarily acetaldehyde, isoprene, ethylene, m/p-xylene, toluene, hexal, isopentane, propanal, propylene, trans-2-butene, etc. In particular, the contributions of acetaldehyde, isoprene, ethylene, and hexal species were prominent during the O3 exceeding days. Ratio analysis showed that the B/T ratio in Zhengzhou from May to September ranged from 0.05 to 5.3, with an average value of 1.1 ± 0.6, and the regional VOCs was mainly controlled by the aging air mass with possible long-distance transports. The analysis of the PMF model showed that the major pollution sources to VOC concentration in Zhengzhou were motor vehicle exhaust emission sources and industrial solvent and secondary conversion sources, contributing 25.6% and 25.8%, respectively. The contribution rates of solvent coating sources, oil and gas volatile sources, plant emission sources, industrial solvents, and secondary conversion sources during O3 exceeding days were 5.4%, 4.7%, 3.3%, and 0.7% higher than those during O3 non-exceeding days, respectively. The research showed that the management of VOCs and NOx pollution sources should be strengthened to reduce their contribution to the O3 generation when O3 exceeds the standard.

Interannual and seasonal variability of the air–sea CO2 exchange at Utö in the coastal region of the Baltic Sea

Abstract. Oceans alleviate the accumulation of atmospheric CO2 by absorbing approximately a quarter of all anthropogenic emissions. In the deep oceans, carbon uptake is dominated by aquatic phase chemistry, whereas in biologically active coastal seas the marine ecosystem and biogeochemistry play an important role in the carbon uptake. Coastal seas are hotspots of organic and inorganic matter transport between the land and the oceans, and thus they are important for the marine carbon cycling. In this study, we investigate the net air–sea CO2 exchange at the Utö Atmospheric and Marine Research Station, located at the southern edge of the Archipelago Sea within the Baltic Sea, using the data collected during 2017–2021. The air–sea fluxes of CO2 were measured using the eddy covariance technique, supported by the flux parameterization based on the pCO2 and wind speed measurements. During the spring–summer months (April–August), the sea was gaining carbon dioxide from the atmosphere, with the highest monthly sink fluxes typically occurring in May, being −0.26 µmol m−2 s−1 on average. The sea was releasing the CO2 to the atmosphere in September–March, and the highest source fluxes were typically observed in September, being 0.42 µmol m−2 s−1 on average. On an annual basis, the study region was found to be a net source of atmospheric CO2, and on average, the annual net exchange was 27.1 gC m−1 yr−1, which is comparable to the exchange observed in the Gulf of Bothnia, the Baltic Sea. The annual net air–sea CO2 exchanges varied between 18.2 (2018) and 39.1 gC m−1 yr−1 (2017). During the coldest year, 2017, the spring–summer sink fluxes remained low compared to the other years, as a result of relatively high seawater pCO2 in summer, which never fell below 220 µatm during that year. The spring–summer phytoplankton blooms of 2017 were weak, possibly due to the cloudy summer and deeply mixed surface layer, which restrained the photosynthetic fixation of dissolved inorganic carbon in the surface waters. The algal blooms in spring–summer 2018 and the consequent pCO2 drawdown were strong, fueled by high pre-spring nutrient concentrations. The systematic positive annual CO2 balances suggest that our coastal study site is affected by carbon flows originating from elsewhere, possibly as organic carbon, which is remineralized and released to the atmosphere as CO2. This coastal source of CO2 fueled by the organic matter originating probably from land ecosystems stresses the importance of understanding the carbon cycling in the land–sea continuum.

A piezoelectric oscillator for low-speed wind energy collection

ABSTRACT The exploration of low-speed wind harvesters is key to harnessing wind energy and enabling self-powered Internet of Things (IoT) sensor networks. In this paper, a piezoelectric oscillator (APO) for low-speed wind energy collection (B-WEC) is proposed and applied to low-speed wind energy harvesting. The magnetic model and power generation characteristics of the harvester are formulated, the magnetic torque is simulated, and the performance under different parameter combinations is tested experimentally. The results show that the APO rotation reduces the magnetic resistive torque by 26.00%. The highest peak-to-peak voltage is 92.35 V for an APO magnet diameter is 8 mm and the angle of the stopper (γ) is 60°, which is 1258.09% higher than fixed (γ is non-existent). Moreover, it has a maximum power of 0.56 mW at 4 m/s, and the optimally matched resistance is 200 kΩ. The harvester can light up about 150 LEDs. The B-WEC designed in this paper can effectively reduce the driving wind speed of piezoelectric wind energy harvesters and can be used as a power source for self-powered networked sensor networks. This study provides a new solution idea for the exploitation of wind energy.

Windbreak Effectiveness of Single and Double-Arranged Shelterbelts: A Parametric Study Using Large Eddy Simulation

Shelterbelts provide essential protection against wind erosion and soil degradation, as well as protection for fruit-bearing plants and crops from strong winds. Enhancing their sheltering capabilities requires optimizing their pattern and orientation, as well as defining their height and desired canopy shape, according to the desired performance. In this work, Large Eddy Simulation is employed to investigate the flow field and windbreak effectiveness of single and double-arranged shelterbelts characterized by different geometry and resistance to the air passage for neutral atmospheric condition. In particular, the canopy of the shelterbelts is modeled as an isotropic porous medium immersed in atmospheric boundary layer flow using the Darcy–Forchheimer model. Results show that a shelterbelt with a rectangular-shaped cross-section and a large canopy height results in the most significant reduction in mean wind speed and TKE, thus providing a large wind-protection region. As the spacing distance of double-arranged shelterbelts increases, the protection zones formed by both shelterbelts are reduced. The systematic comparisons of flow patterns, drag force coefficients, and windbreak effectiveness indicators of a series of single and double-arranged shelterbelts are essential for optimizing the design and management of shelterbelts.

Changing windstorm characteristics over the US Northeast in a single model large ensemble

Abstract Extreme windstorms pose a significant hazard to infrastructure and public safety, particularly in the highly populated US Northeast (NE). However, the influence climate change and changing land use will have on these events remains unclear. A large ensemble generated using the Max-Planck Institute (MPI) Earth system model is used to generate projections of NE windstorms under different shared socioeconomic pathways (SSPs) and to attribute changes to projected land use land cover (LULC) change, externally forced changes and internal climate variability. To reduce the influence of coarse grid cell resolution and uncertainties in surface roughness lengths, windstorms are identified using simultaneous widespread exceedance of local 99th percentile 10 m wind speeds (U99). Projected declines in forest cover in the NE and the resulting reductions in surface roughness length under SSP3-7.0 lead to projections of large increases in U99 and derived windstorm intensity and scale. However, these projected changes in regional LULC under SSP3-7.0 are unprecedented in a historical context and may not be realistic. After corrections are applied to remove the influence of LULC on wind speeds, regionally averaged U99 exhibit declines for most of the single model initial-condition large ensemble (SMILE) members which are broadly proportional to the radiative forcing and global air temperature increase in the SSPs, with a median value of −0.15 ms−1 °C−1. While weak cyclones are projected to decline in frequency in the NE, intense cyclones and the resulting windstorms and indices of socioeconomic loss do not. Where present, significant trends in these loss indices are positive, and some MPI SMILE members generate future windstorms that are unprecedented in the historical period.

Statistical modeling of the effects of wind speed, air temperature and relative humidity on the concentration of carbon monoxide in the urban atmosphere

The high carbon monoxide content in the urban atmosphere is one of the most important indicators of poor air quality in megacities such as Moscow. This study is to evaluate the importance of wind speed, air temperature, and relative air humidity for predicting the concentrations of carbon monoxide for the day ahead using a simplified one-dimensional quasistationary statistical model. It is shown that the concentration of carbon monoxide in the Moscow atmosphere is determined by a combination of internal (previous days CO concentration) and external (meteorological conditions) factors. The variation of carbon monoxide concentration at one station differs from the variation at another station due to the differences in local conditions. Taking into account wind speed and air temperature increases the predictive value of the onedimensional quasi-stationary statistical model for most of the stations. In contrast to wind, relative air humidity decreases the predictive value of the model for most of the stations. This means that meteorological factors considered in this study could have different effects on predicting carbon monoxide concentration in the case of Moscow. The data from the Balchug weather station, located in the city center, offers a more accurate CO concentration forecast for most Moscow stations compared to the VDNKh weather station. For a more complete description of the influence of meteorological conditions on the predicted low concentration of gases, it is useful to take into account the model wind direction, surface air pressure, and the intensity of mixing in the urban boundary layer.

The Impact of Urban Roughness on Outdoor Thermal Comfort in Hot Arid Climate

The proportion of the world's population living in cities is rapidly increasing, presenting new challenges to the urban environment and quality of life. Among these challenges is the urban environment's impact on residents' outdoor thermal comfort, which can affect their health and well-being. Urban roughness, which includes building and street heights, has a significant impact on the thermal environment of urban areas. Changes in these factors cause variations in temperature distribution, wind speed, and humidity, which affect how people perceive thermal conditions. The research problem is the effect of urban roughness on outdoor thermal comfort in hot arid climate, specifically the height of buildings and the density, for a case study in (Al-Bab Al-Sharqi) in Baghdad city. Measurement method that employed the computer programs (Rino8 and Grasshopper) to calculate the thermal comfort index (UTCI) and its impact on various climatic variables. The findings revealed that the thermal comfort index and the climatic factors associated with it vary depending on the configuration of buildings.

Field observation and cause investigation of low-frequency cable vibrations in a cable-stayed bridge

Considerable levels of stay cable vibration in cable-supported bridges pose significant challenges for safety, operation, and maintenance. An accurate understanding of the specific causes and types of vibration is essential for effectively addressing this problem with an optimal design for mitigation measures. This study aims to investigate the cause of an extraordinary low-frequency vibration that occurred at a stay cable by employing comprehensive methodologies. The modal properties and vibrational characteristics of cable vibrations were first obtained through finite element modeling, operational modal analysis, and vision-based displacement measurements. Subsequently, the primary cause of low-frequency vibrations was investigated using field observations and wind tunnel tests. Field-monitoring data revealed that stay cables were excited by small deck vibrations. The critical wind speeds at which deck vibrations occurred in a wind tunnel experiment were aligned with the ranges of wind speeds that produce low-frequency cable vibrations, which confirmed that these two phenomena are manifested under similar wind conditions. A calculated support excitation amplitude, determined via the use of an empirical equation, exhibited consistency with vision-based measured displacement obtained in field experiments. These findings underscore the potential risk of support excitation between minor deck vibrations and cables, which provides valuable insights that could mitigate cable vibrations.

Safety analysis of wind-induced resuspension of radioactive materials from the Techensky reservoir cascade coastline

The paper provides an assessment of the probability and consequences of external events capable of leading to an incident with a breach of established safety barriers restricting the spread of radioactive substances along the coastline of the Techensky reservoir cascade. The initiating events for such an incident could be adverse meteorological conditions resulting in a decrease in the water level in the cascade water bodies below the specified design levels. Drying out the surface of previously inundated sections of the coastline will create an extensive source of atmospheric contamination by technogenic radionuclides during wind-induced resuspension. The probable area of the dried-out sections will be 0.02 — 0.08 kml for reservoir V-3, 0.06 — 0.28 kml for reservoir V-4, 0.19 — 0.90 kml for reservoir V-10, and 0.47 — 2.19 kml for reservoir V-11. The cumulative inventory of 137Cs and 90Sr in the specified area exceeds 105 Bq/ml. The activity released into the atmosphere within an hour will range from 3.69∙108 to 8.48∙1011 Bq for the wind speeds of 5 m/s and 20 m/s, respectively. The probabilities of joint occurrence of strong winds and drought conditions are 6.8∙10-7 per year for a wind speed of 20 m/s and 2.1∙10-3 per year for a wind speed of 5 m/s. The highest values of effective dose for the population of the nearest settlements, reaching 1-3 μSv in the first 10 days and over 200 μSv in the first year, are achieved under extreme wind loads with wind speeds of 20 m/s or more.

Evaluation of wind potential and economic prospects in the southern regions of Morocco with MERRA-2 and ERA5

Meteorological reanalyses are generating interest in the modelling of wind energy production. This study provides a comparison between ERA5 and MERRA-2. Two regions in Morocco were studied: Laayoune-Sakia-El-Hamra (22 sites) and Dakhla-Oued-Eddahab (17 sites). Among the 150 wind turbines tested, the two most efficient ones were identified for each site. The results show that MERRA-2 outperforms ERA5 in several aspects, including scatter plots and R2 correlation. Wind potential and economic viability were evaluated for each of the 39 sites, incorporating a loss factor of 26.71% and applying Weibull parameters to precisely characterize each site. The analysis reveals that a few sites stand out for their high wind speeds, significant power densities and a capacity factor reaching 54.37%. Economically, the cost of electricity, ranging between $ 0.012 and $ 0.023 per kWh, is particularly favourable for wind energy production.

Impact of a two-dimensional steep hill on wind turbine noise propagation

Abstract. Wind turbine noise propagation in a hilly terrain is studied through numerical simulation in different scenarios. Linearized Euler equations are solved in a moving frame that follows the wavefront, and wind turbine noise is modeled with an extended moving source. We employ large-eddy simulations to simulate the flow around the hill and the wind turbine. The sound pressure levels (SPLs) obtained for a wind turbine in front of a 2D hill and a wind turbine on a hilltop are compared to a baseline flat case. First, the source height and wind speed strongly affect sound propagation downwind. We find that topography influences the wake shape, inducing changes in the sound propagation that drastically modify the SPL downwind. Placing the turbine on the hilltop increases the average sound pressure level and amplitude modulation downwind. For the wind turbine placed upstream of a hill, a strong shielding effect is observed. But, because of the refraction by the wind gradient, levels are comparable with the baseline flat case just after the hill. Thus, considering how terrain topography alters the flow and wind turbine wake is essential to accurately predict wind turbine noise propagation.

Numerical modeling and analysis of multistage gear transmission system in wind turbine gearbox under load uncertainty

A multistage gear transmission system (MGTS) is widely used in wind turbine gearboxes. Its dynamic responses are closely related to stochastic aerodynamic torque that has a wide interval range between cut-in and cut-out wind speeds. However, most of the dynamic models of MGTS lack attention to dynamic responses caused by uncertainties of multiple parameters under varied input torque. In this study, a dynamic modeling method in conjunction with interval uncertain analysis of MGTS caused by multiple load-related parameters due to varied input torques is proposed. A comprehensive dynamic model of MGTS is developed considering time-varying gear mesh transmission error, load-related gear mesh stiffness and bearing stiffness as well as flexible shafts. The reasonability of simulation is validated, and then the effects of multiple load-related parameters on uncertain dynamic responses are discussed in-depth. Results show that both gear mesh stiffness and bearing stiffness are enlarged with increasing input torque to cause uncertain dynamic responses. These uncertainties would propagate along the flexible shaft and be suppressed by system rigidity. The dominant transverse vibration modes of shafts are sensitive to bearing stiffness, thus, uncertain bearing stiffness has remarkable effects on dynamic responses than gear mesh stiffness.

Strain response prediction of offshore wind turbine tower under free vibration

Strain monitoring of critical locations in offshore wind turbine tower is essential for fatigue life prediction and safe operation of wind turbine. This paper theoretically analyses the feasibility of predicting tower strain under free vibration using the modal superposition method. A finite element numerical model of the tower is established, and the displacement mode and strain mode parameters of the tower are extracted. The initial displacement and strain of the tower at cut-out wind speed are analyzed, and the first three modal coordinate of the tower are calculated using test points of displacement or strain separately. Subsequently, the full field strain of the tower is predicted using the modal superposition method. Comparative results demonstrate higher accuracy in modal coordinate values calculated using test points of displacement, and the accuracy and errors of strain prediction using different numbers of test points are compared, emphasizing the crucial role of selecting optimal positions and numbers of test points in improving prediction accuracy.

Experimental and numerical analysis of NREL S823 airfoil with auxiliary airfoil integration

ABSTRACT This study explores the potential for using an auxiliary airfoil to improve the aerodynamic characteristics of an NREL (National Renewable Energy Laboratory) S823 airfoil designed explicitly for small-scale wind turbines. The experimental investigation is carried out on a standard S823 airfoil (S823(S)) and an S823 with the inclusion of an auxiliary airfoil (S823(A)), at different angles of attacks (0º-20º) and different low wind speeds (4–10 m/s). The result shows that the S823(A) model consistently outperforms the S823(S) model in terms of lift-related metrics. It exhibited a 10% increase in CL values. Specifically, the S823(A) model achieved a maximum CL value of 1.284 at 10 m/s, which is an 8% improvement over the S823(S) model’s maximum of 1.191 at the same wind speed. Besides, the S823(A) model demonstrated superior performance in the sliding ratio, with values consistently higher by approximately 11%, 9%, 10%, and 14% at wind speeds of 4 m/s, 6 m/s, 8 m/s, and 10 m/s, respectively. Moreover, numerical simulations were carried out for both the models at similar conditions, which reveal that using an auxiliary airfoil reduces flow separation on the pressure side, along with well controlling the Turbulence Kinetic Energy (TKE) dissipation of the S823(A) model. Moreover, the airfoil flow visualization reveals that the auxiliary airfoil promotes smoother airflow separation, especially near the trailing edge of the airfoil. This reduces turbulence and improves the overall stability and controllability of the airfoil across different AoAs. Overall, the S823(A) model offers superior aerodynamic performance, improved lift-to-drag ratio, and better control characteristics across a range of angles of attack compared to the S823(S) model that improves the overall aerodynamic performance of the main airfoil.

Wind Speed Modeling under Quasi‐Linear Autoregressive Neural Network Model for Prediction of Production Power

Accurate wind speed modeling is beneficial for the design of wind energy conversion systems. Models of wind speed are used to assess the adequacy and dependability of a power supply. However, precise wind speed modeling is challenging due to the sporadic availability of wind speed. In this note, we propose a wind speed model with an autoregressive (AR) structure. A hybrid model is developed under linear and nonlinear parts based on a quasi‐linear autoregressive exogenous neural network (Q‐ARX‐NN). The model's structure is composed of a regression vector and its coefficients. The coefficients are divided into linear and nonlinear coefficients. A set of linear coefficients is identified under the algorithm of least square error (LSE), and a set of nonlinear coefficients is modeled by using a neural network to refine the residual error of the nonlinear part. Some artificial neural network (ANN) models can be set as nonlinear part sub‐models to sharpen the model's accuracy. The proposed model is tested for wind speed modeling to estimate wind energy production. Various nonlinear parts of the sub‐model are tested, such as neural networks, radial basis function networks, and ANN networks. Moreover, we evaluate the effects of the order of the model by varying hidden and output nodes, which can be summarized as the number of coefficients of the regression vector. Using specific wind turbine performance data, prediction models estimate production power. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Characteristics and Driving Factors of Energy Balance over Different Underlying Surfaces in the Qinghai Plateau

The study of the surface energy balance characteristics of different ecosystems in the Qinghai Plateau is of great significance for a deeper understanding of land surface processes, the water cycle, and global climate change. This study aims to compare the seasonal variations in energy balance and partitioning of four typical ecosystems on the Qinghai Plateau—swamp meadows, subalpine mountain meadows, alpine shrublands, and alpine deserts. Mantel analysis and path analysis were used to explore the regulatory mechanisms of meteorological elements on energy fluxes and the Bowen ratio (β). The results showed the following: (1) Net radiation (Rn), sensible heat flux (H), and latent heat flux (LE) all exhibited a single-peak pattern of change, and the energy partitioning was closely related to the hydrothermal conditions. Swamp meadows and subalpine mountain meadows were dominated by LE throughout the year and the growing season, while H dominated in the non-growing season. Meanwhile, alpine shrublands and alpine deserts were dominated by H throughout the year. (2) β reflected the characteristics of turbulent fluxes variations and the moisture level of the underlying surface. Swamp meadows and subalpine mountain meadows were relatively moist, with the value of β all being less than 1. Alpine shrublands and deserts were comparatively arid, with the values of β all exceeding 1. The energy closure rate ranged from 48% to 90%, with better energy closure conditions observed during the growing season compared to the non-growing season. (3) Meteorological factors collectively regulated the variations in energy fluxes and its partitioning, with H and LE being primarily influenced by Rn, relative humidity (RH), and soil moisture (Ms). β was significantly affected by RH, Ms, and the saturated vapor pressure deficit (VPD). The sensitivity of the ecosystems to changes in fluxes increased with decreasing moisture, especially in alpine deserts, with Ms, VPD and RH being the most affected. Swamp meadows were significantly associated with air temperature (Ta), soil temperature (Ts), and wind speed; subalpine mountain meadows with Ta and Ts; and alpine shrublands with Ta. These results provided a basis for further analyses of the energy balance characteristics and partitioning differences of different ecosystems on the Qinghai Plateau.

A Tunnel Fire Detection Method Based on an Improved Dempster-Shafer Evidence Theory

Tunnel fires are generally detected using various sensors, including measuring temperature, CO concentration, and smoke concentration. To address the ambiguity and inconsistency in multi-sensor data, this paper proposes a tunnel fire detection method based on an improved Dempster-Shafer (DS) evidence theory for multi-sensor data fusion. To solve the problem of evidence conflict in the DS theory, a two-level multi-sensor data fusion framework is adopted. The first level of fusion involves feature fusion of the same type of sensor data, removing ambiguous data to obtain characteristic data, and calculating the basic probability assignment (BPA) function through the feature interval. The second-level fusion derives basic probability numbers from the BPA, calculates the degree of evidence conflict, normalizes the BPA to obtain the relative conflict degree, and optimizes the BPA using the trust coefficient. The classical DS evidence theory is then used to integrate and obtain the probability of tunnel fire occurrence. Different heat release rates, tunnel wind speeds, and fire locations are set, forming six fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that there is a 67.5%, 83.5%, 76.8%, 83%, 79.6%, and 84.1% probability of detecting fire when it occurs, respectively, and identifies fire occurrence in approximately 2.4 s, an improvement from 64.7% to 70% over traditional methods. This demonstrates the feasibility and superiority of the proposed method, highlighting its significant importance in ensuring personnel safety.

A neural network model for predicting the temperature of insulated overhead lines

In this work, we develop a neural network model based on experimental heating and cooling curves of insulated wires of overhead lines under changes in wind speed and its direction relative to the wire axis. Insulated SIP-3 wire used in overhead lines was investigated. A multilayer perceptron neural network was employed to model the process of heating and cooling of insulated wire under changes in wind speed and its direction. The average absolute error was taken as a criterion for evaluating the prediction accuracy of wire core and insulation temperatures. The main parameters of the developed neural network model include the number of hidden layers and neurons in each hidden layer, the degree of regulation, and the regulatory rigidity of the model. The modelled heating and cooling curves of insulated wire were compared with those obtained experimentally. The average absolute error was equal to 1.74 and –4.08°C for the predicted core and insulation temperatures, respectively. The difference between the heating curves at low wind speeds was found to range within 9°C. It was shown that an increase in wind speed leads to a decrease in the difference between the curves. Our analysis showed that neural network models used for predicting variations in the temperature of insulated overhead lines should be trained using a larger number of input parameters. This is the main prerequisite for high prediction accuracy of such models, when the difference between the simulated and experimental data does not exceed 5%.

The impact of extreme wind conditions and yaw misalignment on the aeroelastic responses of a parked offshore wind turbine

As global energy demands rise and the urgent need to reduce carbon emissions, offshore wind energy technology has evolved rapidly, becoming a vital component of the world’s energy portfolio. However, offshore wind turbines face new challenges due to complex marine environments. Under extreme conditions such as strong winds and yaw misalignment, parked wind turbines exhibit complex aerodynamic and structural responses that significantly differ from those in normal operating conditions. These responses, crucial to the integrity and safety of wind turbines, demand thorough analysis. This article presents an in-depth aeroelastic analysis of a parked 5 MW offshore wind turbine using a sophisticated and precise numerical model. It explores the impacts of incoming wind speed and yaw misalignment on the aerodynamic loads and structural behaviors of the wind turbine. The findings indicate that gravity plays a significant role in blade structural responses for a parked wind turbine. Additionally, higher wind speeds elevate aerodynamic forces, leading to more pronounced fluctuations in root forces and tip deflections. The increase in wind speed triggers and exacerbates high-frequency edgewise vibrations of the blade, further leading to fluctuations and instability in the loads. The study also reveals that yaw misalignment causes the AOA changes significantly over time, and generates intermittent fluctuations in lift and drag, leading to extra blade vibrations with the maximum amplitude exceeding 10% of the blade length. This research enhances the current understanding of the operational safety of offshore wind turbines, particularly under extreme conditions. It aims to provide valuable insights and guidance for researchers and developers in designing and monitoring the performance of offshore wind turbines, ensuring their resilience and efficiency in challenging environments.

Scenario-based stochastic optimization on the variability of solar and wind for component sizing of integrated energy systems

The inherent intermittency and variability of renewable energy sources present significant challenges to the optimal design and implementation of integrated energy systems (IES). This paper introduces a novel stochastic optimization model that integrates advanced scenario generation and clustering algorithm for renewable energy sources within a multi-objective, bi-level optimization framework. Specifically, the clearness index is employed to represent the stochastic distribution of solar radiation intensity by beta distribution, while wind speed uncertainty is modeled seasonally using the Weibull distribution. Monte Carlo sampling with synchronous back substitution is applied for scenario generation and reduction of solar radiation and wind speed. To address the multi-objective evaluation, the analytic hierarchy process is utilized, and the joint optimization is achieved by combining a region contraction algorithm with stochastic programming. The proposed methodology is validated on an IES featuring various heating devices, incorporating uncertainties in both wind and solar energy. The results indicate that the absorption heat pump-based scheme achieves superior energy-saving performance, achieving an energy rate of 0.4724. Additionally, the compression heat pump-based scheme exhibits excellent economic efficiency and environmental sustainability, with a cost of energy of 0.3639 and a renewable fraction of 0.5536.