Wind Temperature Research Articles

Correlation analysis of wind power and meteorology based on the ramp detection method with adaptive sliding window

Abstract Since the penetration of wind power rises rapidly, the coupling between wind power and meteorology strengthens the dependence of the new power system on meteorology. The fluctuation of wind power and the ramp events become key challenges that affect the safe and stable operation of the power system. To balance the accuracy and efficiency of wind power ramp event detection, an adaptive sliding time window-based ramp event detection method is proposed, which includes three major steps: dataset preprocessing and compression, wind power ramp trend pattern extraction considering bump event screening, and wind power ramp detection based on adaptive sliding time window. The correlation between the ramp event of wind power and meteorological factors like temperature, wind speed and direction are analyzed, and the feasibility of the proposed method is verified. The results show that wind speed is the decisive meteorological factor influencing ramp events, and wind power is also affected by wind direction. However, the correlation between wind power and temperature is weak, which only works in a few ramp events.

Dynamics of dispersal: Modeling the impact of dandelion proliferation on environmental and economic equilibria

Dandelion, with its unique wind dispersal mechanism, can easily spread its seeds across vast land areas, encroaching on territories of other creatures. To address this issue, two models are considered: The first model considers wind patterns, temperature, and moisture in dandelion growth, using differential equations to analyze population changes over time. Sensitivity analysis reveals the models responsiveness to these factors. The second model combines ecological and economic factors to assess the impact of invasive species, employing fuzzy mathematics to determine the severity of impact. The study identifies Canada Goldenrod as particularly destructive. These models offer insights for future invasive species management policies, showcasing innovations in incorporating partial differential functions and fuzzy mathematics for more accurate estimations.

ANALISIS POLA CURAH HUJAN DI WILAYAH SUMATERA BAGIAN UTARA MENGGUNAKAN FAST FOURIER TRANSFORM (FFT)

This research was conducted in North Sumatra, namely at Iskandar Muda, Malikussaleh and Deli Serdang stations in the period 1885 to 2019. The purpose of the study was to analyze seasonal and interannual rainfall patterns. The method used is Fast Fourier Transform (FFT) and correlation between rainfall, sea surface temperature and wind. The results of the study showed that the rainfall pattern at Iskandar Muda, Malikussaleh and Deli Serdang stations had two peaks of rainfall. The peak rainfall of the three stations occurs in May and the second peak has differences between Deli Serdang and Iskandar Muda and Malikussaleh stations. Where the peaks of high periodization are only in the 6-month and 12-month periods. There is also a peak periodogram from each station seen in 18-30 months. Rainfall periodogram analysis shows the magnitude of data frequency for each phase cycle and its frequency. The correlation between rainfall, wind and sea surface temperature (SST) at the three stations has a different pattern. At Deli Serdang station, there is an influence of ENSO and IOD, while at Iskandar Muda and Malikussaleh stations there is no influence of ENSO and IOD. Then in the September, October, November (SON) season at the three stations there is no visible influence of ENSO and IOD.

Ocean Surface Warming and Cooling Responses and Feedback Processes Associated With Polar Lows Over the Nordic Seas

AbstractStrong surface winds induced by polar lows (PLs) may affect the upper ocean. However, understanding of the oceanic responses and feedback processes associated with PLs remains insufficient, especially for observations. Using a combined analysis of satellite‐based sea surface temperature (SST) and PL tracking data, we investigated the oceanic response to 380 PL passages over the Nordic Sea occurring between 1999 and 2018. Consequently, two types of oceanic responses—warming and cooling—occurred in 32% and 40% of the total occurrences, respectively. The average magnitude of SST response was approximately ±0.2 K. Significant differences in upward surface turbulent heat flux (THF) between warming and cooling response cases were found, causing a significant difference in the decay rate after maximum PL development. By analyzing changes in the state variables of the THF, we identified two different feedback processes depending on the oceanic warming/cooling response. During a warming (cooling) response, the atmosphere near the surface becomes more unstable (stable), and the turbulence of the marine atmospheric boundary layer increases (decreases), which strengthens (weakens) the ocean surface wind and decreases (increases) temperature and specific humidity. These changes contribute to increasing (decreasing) the upward THF that influences PL development. The differences between these two responses may be caused by the state of the upper ocean layer, including temperature inversion. The analysis of the in situ observations of the upper ocean supports the hypothesis that a warming response occurs when inversion is strong. This study emphasizes the importance of feedback through oceanic responses for understanding and predicting PL.

Cooling Improvement for High-Power-Density Shell-Mounted Underwater Propulsion Motors with Heat Bridges

Subject to an autonomous underwater vehicle (AUV) with rigorously limited space and weight, the high-power-density propulsion motor urgently needs an efficient cooling method to improve reliability and stability. In this paper, a cooling improvement method based on heat bridges (HBs) is proposed for the shell-mounted propulsion motor (SmPM) of the AUVs. First, the electromagnetic and thermal characteristics of a 150 kW SmPM are analyzed using a numerical method. Then, a prototype was developed and tested to verify the accuracy of the numerical calculation. Subsequently, in order to further improve the cooling performance of the motor with minimal weight increment, this paper proposes HBs mounted on the end winding. The maximum winding temperature of the motor containing the proposed HBs is decreased by 20 K at the rated operation state. Based on the validated numerical method, the effects of topologies, materials, and geometric parameters on the cooling effect are investigated. Furthermore, according to the required operating time, the SmPM is optimized based on the cooling performance improvement provided by the proposed HBs. The results show that in addition to the benefit of the cooling improvement contributed by the proposed HB, the weight of the propulsion motor is reduced by 7.14%.

Framework for assessing the performance of overhead transmission lines under wind-temperature effects

This paper presents a framework for evaluating the wind-induced resistance of overhead transmission lines (OTLs), aiming to overcome the limitations associated with disregarding temperature effects and relying on a single metric for failure assessment. The proposed framework factors in temperature influence, carefully considers applied loads and structural characteristics, and accurately calculates failure probability based on a specific case study. Meteorological data is initially collected to create a probabilistic model of wind speed and temperature distribution, accounting for correlation. Components and systems are graded based on structural characteristics and damage modes. The neural network surrogate model is trained with finite element computations for load-structural response sample sets. The surrogate model, in conjunction with logistic regression, generates samples to determine fragility. Finally, the total structural failure probability and monthly failure likelihood are computed by integrating the joint probability model and fragility. Analyses highlight temperature's significant impact on conductor-ground line safety, validating the proposed framework's rationale.

Understanding the movement and dispersal patterns of released Fopius arisanus (Hymenoptera: Braconidae) parasitoids in a papaya orchard.

Implementation of augmentative biological control requires estimates of parasitoid dispersal from the release point to determine appropriate release density, spacing, and timing. This study evaluated the movement patterns of Fopius arisanus Sonan (Hymenoptera: Braconidae) parasitoids, which have historically been used to control invasive tephritid fruit flies. The wasps were released from the central point, and dispersal was monitored over time using parasitism in sentinel fruit and trap captures at 40 points radiating out from the center (15-240 m). The releases were conducted 4 times during June, July, September, and November 2006. The data showed that there were large declines in dispersal by distance. Parasitism was greatest closest to the release point, within 30 m. Parasitism was also greatest within the first 24 h of the release. After 1 wk, parasitism decreased from 41% to 1.5% within 30 m. These data correlated strongly with trap capture data, which also showed that parasitoid movement favored the SE region of our release site, roughly corresponding to the overall prevailing winds. Wind speed, relative humidity, and temperature all affected parasitoid movement during our trial, indicating the complex environmental factors that can affect release success. This is the first report of dispersal metrics for F. arisanus. Our findings are in agreement with other similar studies on braconid movement generally and suggest that frequent, high-density releases are most effective since the dispersal of F. arisanus is limited and retention in the environment is low. We discuss our results in the context of international augmentative biological control release programs.

Alkaline flux dissociation in blast furnace tuyere bird’s nest: Part 1: CaO dissolution kinetics and slag physicochemical evolution in the coal-coke-slag

In the pursuit of optimizing smelting efficiency and minimizing carbon emissions in blast furnace operations, the strategic introduction of the tuyere alkaline flux injection, particularly in managing the viscosity of the coal-coke-slag in the bird’s nest region, is investigated. This study focused on exploring the dynamics of CaO dissolution within the slag of the coal-coke-slag in the bird’s nest area of a 2200 m3 blast furnace in China, particularly under conditions of instability. Quantitative analysis revealed that at 1550 °C, the CaO maximal dissolution capacity reached 62.05 wt%, with an average rate constant of dissolution observed at 4.65 × 10−4 s−1. The dissolution mechanism was characterized by interfacial dissociation, mass transfer diffusion, and the attainment of a dynamic equilibrium, predominantly influenced by the Ca2+ concentration gradient within the slag. This process facilitates the depolymerization of the slag’s network structure, heavily influenced by the aluminum oxide tetrahedron, effectively reducing slag viscosity. Further, the study quantified the optimal CaO injection content through the tuyere, suggesting a range of 4.00–5.32 kg/tHM, contingent on the use of high-quality raw fuel and advanced high wind temperature technology. This work contributes valuable theoretical insights towards enhancing the efficiency and sustainability of smelting operations, applicable to both conventional and high pellet blast furnaces, marking a significant step forward in the development of low-carbon metallurgical processes.

Impact of Fan Position and Speed Adjustments in Glass Greenhouse Environments

In the context of the contemporary development of precision agriculture, glass greenhouses, as an important infrastructure for enhancing crop production efficiency and quality, have become a frontier area of agricultural science and technology innovation for the internal microenvironmental regulation, especially the optimization of airflow dynamics and temperature distribution. In this paper, the study is based on the three-dimensional turbulence model equations of Reynolds number and Darcy-Forkheimer law, and modeled the cases without and with crops, simulated their different wind velocity and temperature, generated the corresponding simulation diagrams and analyzed them, and the results show that changing the Wind velocity to 3m/s or changing the fan position to 1m is a better condition than that with Wind velocity of 2m/s and fan position of 1.3m. In this study, it was found that both variables, Wind velocity and fan position, could affect the growth of crops in glasshouse, excluding most of the factors.

Multi-Physical Field Analysis and Optimization Design of the High-Speed Motor of an Air Compressor for Hydrogen Oxygen Fuel Cells

The hydrogen oxygen fuel cell is a power source with significant potential for development. The air compressor provides ample oxygen for the fuel cell, and as a key component of the air compressor, the performance of the motor greatly impacts the efficiency of the fuel cell. In order to enhance the system performance of high-speed permanent magnet motors, optimization was conducted on the motor’s geometric dimensions to minimize rotor loss and maximize power density, taking into account the comprehensive constraints of electromagnetic and mechanical properties. The finite-element method was employed to analyze the motor’s performance, conducting a multi-physical field analysis that included electromagnetic field, rotor loss, and mechanical strength analysis, as well as temperature field analysis. Aiming at the problem of high temperature rise in high-speed motor winding, the influence of the cooling water flow rate on the winding temperature rise was analyzed and simulated. Based on the analysis results, the minimum cooling water flow rate was obtained. According to the optimized design results, a prototype of an 18 kW, 100,000 rpm motor was manufactured, and the efficiency and temperature rise were tested. The experimental results verify the correctness and effectiveness of the optimal design.

Association of sudden sensorineural hearing loss with meteorological factors: a time series study in Hefei, China, and a literature review

Air pollution can cause disease and has become a major global environmental problem. It is currently believed that air pollution may be related to the progression of SSNHL. As a rapidly developing city in recent years, Hefei has serious air pollution. In order to explore the correlation between meteorological variables and SSNHL admissions, we conducted this study. This study investigated the short-term associations between SSNHL patients admitted to the hospital and Hefei climatic variables. The daily data on SSNHL-related hospital admissions and meteorological variables containing mean temperature (T-mean; °C), diurnal temperature range (DTR; °C), atmospheric pressure (AP; Hp), and relative humidity (RH; %), from 2014 to 2021 (2558 days), were collected. A time-series analysis integrating distributed lag non-linear models and generalized linear models was used. PubMed, Embase, Cochrane Library, and Web of Science databases were searched. Literature published up to August 2023 was reviewed to explore the potential impact mechanisms of meteorological factors on SSNHL. The mechanisms were determined in detail, focusing on wind speed, air pressure, temperature, humidity, and air pollutants. Using a median of 50.00% as a baseline, the effect of exceedingly low T-mean in the single-day hysteresis effect model began at a lag of 8 days (RR = 1.032, 95% CI: 1.001 ~ 1.064). High DTR affected the admission rate for SSNHL on lag 0 day. The significance of the effect was the greatest on that day (RR = 1.054, 95% CI: 1.007 ~ 1.104) and then gradually decreased. High and exceedingly high RH affected the admission rate SSNHL on lag 0 day, and these effects lasted for 8 and 7 days, respectively. There were significant associations between all grades of AP and SSNHL. This is the first study to assess the effect of meteorological variables on SSNHL-related admissions in China using a time-series approach. Long-term exposures to high DTR, RH values, low T-mean values, and all AP grades enhance the incidence of SSNHL in residents. Limiting exposure to extremes of ambient temperature and humidity may reduce the number of SSNHL-related hospital visits in the region. It is advisable to maintain a suitable living environment temperature and avoid extreme temperature fluctuations and high humidity. During periods of high air pollution, it is recommended to stay indoors and refrain from outdoor exercise.

Machine Learning to Characterize Biogenic Isoprene Emissions and Atmospheric Formaldehyde with Their Environmental Drivers in the Marine Boundary Layer

Oceanic biogenic emissions exert a significant impact on the atmospheric environment within the marine boundary layer (MBL). This study employs the extreme gradient boosting (XGBoost) machine learning method and clustering method combined with satellite observations and model simulations to discuss the effects of marine biogenic emissions on MBL formaldehyde (HCHO). The study reveals that HCHO columnar concentrations peaked in summer with 8.25 × 1015 molec/cm2, but the sea–air exchange processes controlled under the wind and sea surface temperature (SST) made marine biogenic emissions represented by isoprene reach their highest levels in winter with 95.93 nmol/m2/day. Analysis was conducted separately for factors influencing marine biogenic emissions and affecting MBL HCHO. It was found that phytoplankton functional types (PFTs) and biological degradation had a significant impact on marine biogenic emissions, with ratio range of 0.07~15.87 and 1.02~5.42 respectively. Machine learning methods were employed to simulate the conversion process of marine biogenic emissions to HCHO in MBL. Based on the SHAP values of the learning model, the importance results indicate that the factors influencing MBL HCHO mainly included NO2, as well as temperature (T) and relative humidity (RH). Specifically, the influence of NO2 on atmospheric HCHO was 1.3 times that of T and 1.6 times that of RH. Wind speed affected HCHO by influencing both marine biogenic emission and the atmospheric physical conditions. Increased marine biogenic emissions in air masses heavily influenced by human activities can reduce HCHO levels to some extent. However, in areas less affected by human activities, marine biogenic emissions can lead to higher levels of HCHO pollution. This research explores the impact of marine biogenic emissions on the HCHO status of the MBL under different atmospheric chemical conditions, offering significant insights into understanding chemical processes in marine atmospheres.

Wind farm power curve characterization under different atmospheric stability regimes

Wind farm power curves are particularly relevant in different applications. A better understanding of wind farm power curves under different atmospheric regimes is sought. In the present work, the atmospheric stability regime was identified by the vertical gradient of the potential temperature. Vertical profile of the wind velocity presented a clearly different pattern during the stable and unstable regimes. Analysis was performed considering 5 years of data from three wind farms, each corresponding to different mesoscale wind. Power curves are presented as a function of atmospheric stability. For each wind farm, we utilized wind velocity and temperature data measurements of a correspondent near the tower location. Clearly different patterns of wind farm power curves were observed as a function of the stability regime, with clear differences in the dependence of mesoscale wind regimes. For the same mean velocity, wind power production is greater for higher values of turbulence intensity TI.

Effect of background wind and dissipation processes on the semidiurnal component of atmospheric solar tides

The equations governing the neutral wind velocities and temperature, which depict the oscillations of atmospheric tides, incorporate various elements of the atmosphere. These factors comprise background wind, temperature profiles, and the atmosphere’s composition, including ozone, oxygen, carbon dioxide, water, etc. The current work describes the method of solution of the atmospheric tidal model and investigates the effects of background wind and dissipation processes on the migrating semidiurnal tides during the March equinox. Thermal heating due to water vapour absorption in the troposphere, ozone in the stratosphere and oxygen absorption in the thermosphere is considered in this model. Furthermore, the model takes into account the various dissipation processes (i.e. ion drag and Hall coefficients, Newtonian cooling, divergence of momentum and heat fluxes due to molecular and eddy diffusion) and background wind. The semidiurnal tidal features obtained from the present model match well with the Global Scale Wave Model (GSWM-98). The bar charts are presented to quantify the effects of different parameters on horizontal winds for altitudes below 100 km and above 100 km, separately. It reveals that the background wind plays a vital role in deciding the semidiurnal tidal amplitudes of zonal and meridional winds. Momentum and heat flux coefficients also have a significant influence on semidiurnal tides. At higher altitudes (>100 km), the ion drag reduces the amplitude of semidiurnal tides considerably.

Stochastic and Extreme Scenario Generation of Wind Power and Supply–Demand Balance Analysis Considering Wind Power–Temperature Correlation

In the context of large-scale wind power access to the power system, it is urgent to explore new probabilistic supply–demand analysis methods. This paper proposes a wind power stochastic and extreme scenario generation method considering wind power–temperature correlations and carries out probabilistic supply–demand balance analysis based on it. Firstly, the influence of temperature on wind power output is analyzed via Pearson coefficient to obtain the correlation between wind power and temperature. Secondly, based on the historical wind power curve, a large number of wind power output scenarios are randomly generated while fully preserving its characteristics, and probabilistic supply–demand analysis is carried out. Thirdly, for the extreme case of continuous multi-day extreme heat without wind, extreme scenarios are selected from the generated scenarios for supply–demand balance analysis. Finally, a practical example in a province in central-eastern China is used to verify the effectiveness of the proposed method. The results indicate that the scenario generation method can effectively capture the historical wind power characteristics and can be better applied to the diversified supply and demand balance analysis to obtain more accurate analysis results.

Rancang Bangun Sistem Monitoring Cuaca Berbasis Google Spreadsheet Untuk Nelayan Di Wilayah Pesisir Pantai

Coastal areas are vital areas for fishing activities, and the weather in this area plays a very important role in the safety and efficiency of fishermen's operations. Fishermen on the coast often face the challenges of weather that is changeable and can be very extreme, including strong winds that can cause high waves and unsafe sea conditions. Therefore, the development of a Google spreadsheet-based weather monitoring system for fishermen in coastal areas is very relevant and strategic to support safer and more productive fishing activities. This research aims to make it easier for fishermen to monitor the weather in coastal areas in real time. Testing is carried out by running the system for observation for a predetermined time duration. This research uses an anemometer, DHT11 and raindrop sensors connected to a NodeMCU ESP32 microcontroller to send data to a Google spreadsheet, with readings of wind speed, temperature, air humidity and rain values. This research was carried out at the Nusantara Prigi fishing port, Trenggalek, challenging the work of anemometer sensors, DHT11 and raindrop sensors. Testing of tools and data obtained by comparing data on wind speed, temperature and air humidity using a digital anemometer and digital thermometer hygrometer humidity. The results of this research are that a Google spreadsheet-based weather monitoring design for fishermen in coastal areas can monitor wind speed, air humidity, temperature and detect rain online using a spreadsheet application and functions well with an average error accuracy of 3%.

Forecasting and Early Warning of Wind‐Farm‐Tripping Event under Strong‐Wind and Cold‐Wave Conditions Based on Combined Nonparametric Estimation Model and K‐Value Method

Wind power, as a pivotal renewable energy source, is increasingly vital in the global energy structure transformation. However, extreme weather, such as strong winds and cold waves, can result in rapid fluctuations in wind power generation and even lead to large‐scale wind‐farm‐tripping events. Consequently, forecasting and early warning of these events under extreme weather conditions are crucial for ensuring the safe operation of the power system. In this article, a forecasting and early warning method for wind‐farm‐tripping events is proposed, which is based on a multi‐nonparametric estimation approach. In this article, the error distribution of forecasting wind speed and temperature in numerical weather prediction (NWP) is modeled, and interval forecasting for these parameters is conducted. When forecasting intervals of NWP reach the thresholds of wind‐turbine‐tripping protection strategy, it will lead to the wind‐farm‐power‐tripping events. Furthermore, in this article, a method for calculating the wind‐power‐tripping capacity based on the K‐value method is introduced. The effectiveness of the proposed method is verified through actual wind‐power‐tripping events in the wind farm. The methodology proposed in the article provides a solution of early warning and forecasting for strong–wind‐ and cold‐wave wind‐farm‐tripping events.

Pollutants In the Selected 3 – Bus Station Microenvironments in Enugu Metropolis

Idling buses in bus station micro-environments potentially pollute ambient air with health implications for commuters with long waiting times. This study aimed at finding the severity of air pollutants in selected three bus station microenvironments in Enugu metropolis, South Eastern Nigeria with the objective to determine the air quality index. Real time measurements of ambient particulates (PM10 and PM2.5) and gaseous pollutants such as SO2, NO2 and CO were monitored, in conjunction with meteorological parameters (wind speed, temperature and relative humidity) on idling buses in selected three bus stations and a control station in Enugu Metropolis. The study spanned through September 2016 to August 2018, covering dry and wet seasons. Hand-held Aeroqual Series - 200 gas monitor/sensors, particulates laser meter and ambient weather anemometer were used to collect data. Results indicated that the daily highest mean concentrations of pollutants were as follows: PM10=228µg/m3 – Young Shall Grow; PM2.5=99.5µg/m3 – Young-Shall-Grow; SO2=0.81ppm – Ifesinachi; NO2= 0.068ppm – Young-Shall-Grow; CO= 7.376ppm – ABC, consistently maintained higher concentrations than those at the control station. AQI was calculated with AQI calculator. The air quality index (AQI) results showed a severe health issue in the selected 3-bus station micro environments. This implies that commuters are highly at risk if they cluster idling buses at bus stations. This study can assist the relevant authorities to set strict ambient air quality objectives for pollutants in transport micro environments.

The Fan Design Optimization for Totally Enclosed Type Induction Motor with Experimentally Verified CFD-Based MOGA Simulations

The energy efficient electric motors save energy thus reduce operating costs. Since there are several losses affecting the motor efficiency, fan design plays an important role to minimize these losses. This study examines the effects of the design parameters of a radial bladed fan on the motor efficiency in a 132 frame with a power of 7.5 kW. The parametric analysis was carried out with the computational fluid dynamics method, and the results were used for the multiobjective genetic algorithm (MOGA) optimization study with the highest efficiency and the lowest motor body temperature objectives. The hub height, hub radius, distance to body cover, blade rising angle, fan cover entrance distance, blade edge angle, blade center radius, blade edge radius, blade end radius, and center edge distance were selected as optimization parameters. 151 simulations were performed. The results showed that the most important parameter for fan efficiency is the hub height which is the parameter that determines the height of the fan impeller diameter. According to the results, the optimum fan design increased the efficiency by 8% compared to the original fan and reduced the winding temperature by 8 °C. The optimized fan design was manufactured and tested against imulation data. This study contributes to sustainable development goals by improving motor efficiency that reduces the cost, designing of new components, and cooling the fan effectively that reduces the amount of copper used.

Short-term wind speed prediction of wind farm based on TSO-VMD-BiLSTM

Aiming at the random and intermittent characteristics of wind speed, a short-term wind speed prediction (SWSP) method based on TSO-VMD-BiLSTM is proposed in this article. Firstly, open-source historical data from a certain region in 2022, including wind speed, direction, pressure, and temperature is analyzed. The data is processed through variational mode decomposition (VMD) to fully extract feature data from historical wind speed records. Secondly, taking historical wind speed, direction, pressure, and temperature as inputs and wind speed as output, a SWSP model based on long short-term memory (LSTM) network is constructed. Thirdly, the tuna swarm optimization (TSO) algorithm is utilized for parameters optimization, and a bi-directional long short-term memory (BiLSTM) network is incorporated to enhance prediction accuracy for micrometeorological parameters. The proposed TSO-VMD-BiLSTM model is validated through comparison with other models, demonstrating its higher accuracy with the maximum absolute error of only 2.52 m/s, the maximum root mean square error of 0.81, the maximum mean absolute error of only 0.54, and the maximum mean absolute percentage error of 6.89%.