scholarly journals Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2158 ◽  
Author(s):  
Mekalathur B Hemanth Kumar ◽  
Saravanan Balasubramaniyan ◽  
Sanjeevikumar Padmanaban ◽  
Jens Bo Holm-Nielsen
Keyword(s):  
Wind Speed ◽  
Frequency Distribution ◽  
Wind Direction ◽  
Optimization Method ◽  
Wind Data ◽  
Gray Wolf ◽  
Energy Potential ◽  
Weibull Parameters ◽  
Wind Speeds ◽  
Wind Potential

In this paper the multiverse optimization (MVO) was used for estimating Weibull parameters. These parameters were further used to analyze the wind data available at a particular location in the Tirumala region in India. An effort had been made to study the wind potential in this region (13°41′30.4″ N 79°21′34.4″ E) using the Weibull parameters. The wind data had been measured at this site for a period of six years from January 2012 to December 2017. The analysis was performed at two different hub heights of 10 m and 65 m. The frequency distribution of wind speed, wind direction and mean wind speeds were calculated for this region. To compare the performance of the MVO, gray wolf optimizer (GWO), moth flame optimization (MFO), particle swarm optimization (PSO) and other numerical methods were considered. From this study, the performance had been analyzed and the best results were obtained by using the MVO with an error less than one. Along with the Weibull frequency distribution for the selected region, wind direction and wind speed were also provided. From the analysis, wind speed from 2 m/s to 10 m/s was present in sector 260–280° and wind from 0–4 m/s were present in sector 170–180° of the Tirumala region in India.

scholarly journals Wind Power potential in Kigali and Western provinces of Rwanda

2015 ◽  
Vol 2 (1) ◽  
pp. 25-36
Author(s):  
Otieno Fredrick Onyango ◽  
Sibomana Gaston ◽  
Elie Kabende ◽  
Felix Nkunda ◽  
Jared Hera Ndeda
Keyword(s):  
Wind Speed ◽  
Probability Density ◽  
Wind Power ◽  
Wind Direction ◽  
Small Scale ◽  
Energy Potential ◽  
Wind Speeds ◽  
Mean Wind Speed ◽  
Wind Energy Potential ◽  
Low Wind Speeds

Wind speed and wind direction are the most important characteristics for assessing wind energy potential of a location using suitable probability density functions. In this investigation, a hybrid-Weibull probability density function was used to analyze data from Kigali, Gisenyi, and Kamembe stations. Kigali is located in the Eastern side of Rwanda while Gisenyi and Kamembe are to the West. On-site hourly wind speed and wind direction data for the year 2007 were analyzed using Matlab programmes. The annual mean wind speed for Kigali, Gisenyi, and Kamembe sites were determined as 2.36m/s, 2.95m/s and 2.97m/s respectively, while corresponding dominant wind directions for the stations were ,  and  respectively. The annual wind power density of Kigali was found to be  while the power densities for Gisenyi and Kamembe were determined as and . It is clear, the investigated regions are dominated by low wind speeds thus are suitable for small-scale wind power generation especially at Kamembe site.

Assessment of Wind Energy Potential in Three Different Locations of Malaysia

2015 ◽  
Vol 785 ◽  
pp. 621-626
Author(s):  
R. Shamsipour ◽  
M. Fadaeenejad ◽  
M.A.M. Radzi
Keyword(s):  
Wind Speed ◽  
Energy Density ◽  
Wind Energy ◽  
Wind Power ◽  
Weibull Model ◽  
Energy Potential ◽  
Wind Speeds ◽  
Wind Potential ◽  
Mean Wind Speed ◽  
Wind Energy Potential

In this study, wind energy potential in three different stations in Malaysia in period of 5 years is analyzed. Base on Weibull distribution parameters, the mean wind speed, wind power density and wind energy density is estimated for each defined location. Although there are many works about wind potential in Malaysia, however a few of them have been provided a comprehensive study about wind power in different places in Malaysia. According to the findings, the annual mean wind speeds indicates that the highest wind speed variation is about 2 m/s and is belonged to the Subang station and the highest wind speed is 3.5 m/s in in Kudat. It is also found that the maximum wind power densities among these three sites are 22 W/m2, 24 W/m2 and 22 W/m2 in Kudat station in January, February and September respectively. The results of the study show that as the second parameter for Weibull model, the highest wind energy density has been 190 kWh/m2 per year in Kudat and the lowest one has been about 60 kWh/m2 in Kuching.

scholarly journals Assessment of Offshore Wind Characteristics and Wind Energy Potential in Bohai Bay, China

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2879 ◽  
Author(s):  
Jianxing Yu ◽  
Yiqin Fu ◽  
Yang Yu ◽  
Shibo Wu ◽  
Yuanda Wu ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Direction ◽  
Resource Assessment ◽  
Offshore Wind ◽  
Bohai Bay ◽  
Energy Potential ◽  
Wind Potential ◽  
Wind Energy Potential ◽  
Wind Characteristics

Wind energy, one of the most sustainable renewable energy sources, has been extensively developed worldwide. However, owing to the strong regional and seasonal differences, it is necessary to first evaluate wind energy resources in detail. In this study, the offshore wind characteristics and wind energy potential of Bohai Bay (38.7° N, 118.7° E), China, were statistically analyzed using two-year offshore wind data with a time interval of one second. Furthermore, Nakagami and Rician distributions were used for wind energy resource assessment. The results show that the main wind direction in Bohai Bay is from the east (−15°–45°), with a speed below 12 m/s, mainly ranging from 4 to 8 m/s. The main wind speed ranges in April and October are higher than those in August and December. The night wind speed is generally higher than that in the daytime. The Nakagami and Rician distributions performed reasonably in calculating the wind speed distributions and potential assessments. However, Nakagami distribution provided better wind resource assessment in this region. The wind potential assessment results suggest that Bohai Bay could be considered as a wind class I region, with east as the dominant wind direction.

scholarly journals Bayesian Model Averaging for Wind Speed Ensemble Forecasts Using Wind Speed and Direction

2017 ◽  
Vol 32 (6) ◽  
pp. 2217-2227 ◽  
Author(s):  
Siri Sofie Eide ◽  
John Bjørnar Bremnes ◽  
Ingelin Steinsland
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Bayesian Model Averaging ◽  
Weather Prediction ◽  
Model Averaging ◽  
Strong Wind ◽  
Ensemble Forecasts ◽  
Wind Speeds ◽  
Low Wind Speeds ◽  
The One

Abstract In this paper, probabilistic wind speed forecasts are constructed based on ensemble numerical weather prediction (NWP) forecasts for both wind speed and wind direction. Including other NWP variables in addition to the one subject to forecasting is common for statistical calibration of deterministic forecasts. However, this practice is rarely seen for ensemble forecasts, probably because of a lack of methods. A Bayesian modeling approach (BMA) is adopted, and a flexible model class based on splines is introduced for the mean model. The spline model allows both wind speed and wind direction to be included nonlinearly. The proposed methodology is tested for forecasting hourly maximum 10-min wind speeds based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts at 204 locations in Norway for lead times from +12 to +108 h. An improvement in the continuous ranked probability score is seen for approximately 85% of the locations using the proposed method compared to standard BMA based on only wind speed forecasts. For moderate-to-strong wind the improvement is substantial, while for low wind speeds there is generally less or no improvement. On average, the improvement is 5%. The proposed methodology can be extended to include more NWP variables in the calibration and can also be applied to other variables.

scholarly journals Frequency Distribution Model of Wind Speed Based on the Exponential Polynomial for Wind Farms

2019 ◽  
Vol 11 (3) ◽  
pp. 665 ◽  
Author(s):  
Lingzhi Wang ◽  
Jun Liu ◽  
Fucai Qian
Keyword(s):  
Wind Speed ◽  
Frequency Distribution ◽  
Least Squares Method ◽  
Wind Farms ◽  
Rayleigh Distribution ◽  
Distribution Model ◽  
Exponential Polynomial ◽  
Distribution Models ◽  
Wind Speed Data ◽  
Wind Speeds

This study introduces and analyses existing models of wind speed frequency distribution in wind farms, such as the Weibull distribution model, the Rayleigh distribution model, and the lognormal distribution model. Inspired by the shortcomings of these models, we propose a distribution model based on an exponential polynomial, which can describe the actual wind speed frequency distribution. The fitting error of other common distribution models is too large at zero or low wind speeds. The proposed model can solve this problem. The exponential polynomial distribution model can fit multimodal distribution wind speed data as well as unimodal distribution wind speed data. We used the linear-least-squares method to acquire the parameters for the distribution model. Finally, we carried out contrast simulation experiments to validate the effectiveness and advantages of the proposed distribution model.

scholarly journals Numerical Simulation of the Aeroelastic Response of Wind Turbines in Typhoons Based on the Mesoscale WRF Model

2019 ◽  
Vol 12 (1) ◽  
pp. 34
Author(s):  
Long Wang ◽  
Cheng Chen ◽  
Tongguang Wang ◽  
Weibin Wang
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Direction ◽  
Flow Direction ◽  
Wrf Model ◽  
Normal Operation ◽  
Average Wind Speed ◽  
Aeroelastic Response ◽  
Response Characteristics ◽  
Wind Speeds

A new simulation method for the aeroelastic response of wind turbines under typhoons is proposed. The mesoscale Weather Research and Forecasting (WRF) model was used to simulate a typhoon’s average wind speed field. The measured power spectrum and inverse Fourier transform method were coupled to simulate the pulsating wind speed field. Based on the modal method and beam theory, the wind turbine model was constructed, and the GH-BLADED commercial software package was used to calculate the aerodynamic load and aeroelastic response. The proposed method was applied to assess aeroelastic response characteristics of a commercial 6 MW offshore wind turbine under different wind speeds and direction variation patterns for the case study of typhoon Hagupit (2008), with a maximal wind speed of 230 km/h. The simulation results show that the typhoon’s average wind speed field and turbulence characteristics simulated by the proposed method are in good agreement with the measured values: Their difference in the main flow direction is only 1.7%. The scope of the wind turbine blade in the typhoon is significantly larger than under normal wind, while that under normal operation is higher than that under shutdown, even at low wind speeds. In addition, an abrupt change in wind direction has a significant impact on wind turbine response characteristics. Under normal operation, a sharp variation of the wind direction by 90 degrees in 6 s increases the wind turbine (WT) vibration scope by 27.9% in comparison with the case of permanent wind direction. In particular, the maximum deflection of the wind tower tip in the incoming flow direction reaches 28.4 m, which significantly exceeds the design standard safety threshold.

scholarly journals The Study of Sand-blocking Characteristic and Flow Field Simulation Analysis of the Ring-shaped Sand-barrier

2018 ◽  
Vol 38 ◽  
pp. 01044
Author(s):  
Wei Qiang Zheng ◽  
Wen Jun Wei ◽  
Ping Yi Liu
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Simulation Analysis ◽  
Inertia Force ◽  
Two Phase ◽  
Wind Speeds ◽  
Sand Control ◽  
Before And After ◽  
Sand Flow ◽  
Laminar Flow Model

Aiming at the complexity of wind direction and irregular sand flow in a desert area, a combinatorial ring-shaped sand barrier is used. Stokes law of inertia force and centrifugal force and gravity sedimentation are used. With CFD fluid software Fluent, laminar flow model Equation and κ-ε turbulence model, the wind speed of the sand in the sand-gas-solid two-phase flow passing through the circular sand barrier is studied at different distances and different altitudes after the sand barrier, the wind speeds before and after the sand barrier are compared and analyzed . The mean minimum wind speed behind the single sand barrier was reduced by 32.5% -49.4% compared with that before the sand barrier. The wind speed at different height of the composite sand barrier was reduced by 30% -58.3% compared with the inlet wind speed, which solved the problem of irregular wind and sand control in the desert wind direction.

Alat Ukur Kecepatan Angin Permukaan Air Laut Berbasis Mikrokontroler

2020 ◽  
Vol 2 (2) ◽  
pp. 80-88
Author(s):  
Waluyo Waluyo ◽  
Meli Ruslinar
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Wind Data ◽  
Measuring Instrument ◽  
Ocean Engineering ◽  
Sea Surface Wind Speed ◽  
Wind Speeds ◽  
Sea Surface Wind ◽  
Engineering Modeling

The microcontroller is one technology that is developing so rapidly with various types and functions, one of which is Arduino Uno which can be used as a microcontroller for various functions in the field of electronics technology. This research was conducted at the Laboratory of Ocean Engineering Modeling, Marine and Fisheries Polytechnic of Karawang in March-June 2020. The purpose of this study was to create a microcontroller-based sea surface wind speed measuring instrument. Based on the results of the acquisition of wind data using a fan simulation and natural wind gusts with different wind speeds in the field show a significant tool response. The results of the comparison of data recording between the results of research with the existing wind speed measuring instrument show that there is an average tool error of 3.24%, a relative error of 3.78%, and an instrument accuracy rate of 96.76%. Thus it can be said that the ability of the tool is able to record wind data with high accuracy.

Anemotactic strategy and optimum flight conditions for host-plant finding in the cabbage root fly, Delia radicum (L.) (Diptera: Anthomyiidae)

1987 ◽  
Vol 77 (2) ◽  
pp. 271-277 ◽  
Author(s):  
S. F. Nottingham
Keyword(s):  
Wind Speed ◽  
Host Plant ◽  
Linear Model ◽  
Wind Direction ◽  
Generalized Linear Model ◽  
Delia Radicum ◽  
Trap Catch ◽  
Local Wind ◽  
Wind Speeds ◽  
Trap Catches

AbstractHourly trap catches of Delia radicum (L.) from opposite sides of host-plant (cabbage) and non-host-plant (carrot) plots in eastern England were analysed with respect to local wind direction, wind speed, temperature and humidity. More females were caught around host- than non-host-plant plots. They predominantly approached host-plant plots by upwind movement, but equal upwind and downwind movement occurred to non-host-plant plots. A generalized linear model revealed that wind speed and humidity influenced the total trap catch of females, with wind speeds below 2 m/s and relative humidities above 65% being most favourable to fly activity, while wind speed was the only variable having a significant effect on the trap catches of flies moving upwind to host-plant plots.

scholarly journals One decade of parallel fine (PM<sub>2.5</sub>) and coarse (PM<sub>10</sub>–PM<sub>2.5</sub>) particulate matter measurements in Europe: trends and variability

2012 ◽  
Vol 12 (7) ◽  
pp. 3189-3203 ◽  
Author(s):  
I. Barmpadimos ◽  
J. Keller ◽  
D. Oderbolz ◽  
C. Hueglin ◽  
A. S. H. Prévôt
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Stationary Point ◽  
Generalized Additive Models ◽  
Negative Relationship ◽  
Additive Models ◽  
Wind Speeds ◽  
East Wind ◽  
Pm10 And Pm2.5 ◽  
Meteorological Observations

Abstract. The trends and variability of PM10, PM2.5 and PMcoarse concentrations at seven urban and rural background stations in five European countries for the period between 1998 and 2010 were investigated. Collocated or nearby PM measurements and meteorological observations were used in order to construct Generalized Additive Models, which model the effect of each meteorological variable on PM concentrations. In agreement with previous findings, the most important meteorological variables affecting PM concentrations were wind speed, wind direction, boundary layer depth, precipitation, temperature and number of consecutive days with synoptic weather patterns that favor high PM concentrations. Temperature has a negative relationship to PM2.5 concentrations for low temperatures and a positive relationship for high temperatures. The stationary point of this relationship varies between 5 and 15 °C depending on the station. PMcoarse concentrations increase for increasing temperatures almost throughout the temperature range. Wind speed has a monotonic relationship to PM2.5 except for one station, which exhibits a stationary point. Considering PMcoarse, concentrations tend to increase or stabilize for large wind speeds at most stations. It was also observed that at all stations except one, higher PM2.5 concentrations occurred for east wind direction, compared to west wind direction. Meteorologically adjusted PM time series were produced by removing most of the PM variability due to meteorology. It was found that PM10 and PM2.5 concentrations decrease at most stations. The average trends of the raw and meteorologically adjusted data are −0.4 μg m−3 yr−1 for PM10 and PM2.5 size fractions. PMcoarse have much smaller trends and after averaging over all stations, no significant trend was detected at the 95% level of confidence. It is suggested that decreasing PMcoarse in addition to PM2.5 can result in a faster decrease of PM10 in the future. The trends of the 90th quantile of PM10 and PM2.5 concentrations were examined by quantile regression in order to detect long term changes in the occurrence of very large PM concentrations. The meteorologically adjusted trends of the 90th quantile were significantly larger (as an absolute value) on average over all stations (−0.6 μg m−3 yr−1).

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