scholarly journals Data analysis wind monitoring in the Republic of Tatarstan

Author(s):  
E. V. Nasyrova ◽  
N. F. Timerbayev ◽  
O. V. Leukhina ◽  
I. Yu. Mazarov

The paper presents the results of wind monitoring carried out in order to confirm the feasibility of building a wind farm in the Republic of Tatarstan. The task of wind monitoring is to determine and study the dynamics of the average annual wind regime and calculate the wind energy potential at promising sites for placing a wind power plant. On the given sites, after the annual cycle of meteorological parameters measurements, the average annual wind speeds, wind power, preferred directions, wind density, vertical profile of the wind flow and other data necessary for a detailed calculation of the wind power potential of the sites and the selection of specific models of wind generators and their arrangements for operation will be determined at these sites. An important component of the work performed is the development of methods for calculating wind potential at heights other than the heights of direct measurements.

2021 ◽  
pp. 0309524X2110438
Author(s):  
Carlos Méndez ◽  
Yusuf Bicer

The present study analyzes the wind energy potential of Qatar, by generating a wind atlas and a Wind Power Density map for the entire country based on ERA-5 data with over 41 years of measurements. Moreover, the wind speeds’ frequency and direction are analyzed using wind recurrence, Weibull, and wind rose plots. Furthermore, the best location to install a wind farm is selected. The results indicate that, at 100 m height, the mean wind speed fluctuates between 5.6054 and 6.5257 m/s. Similarly, the Wind Power Density results reflect values between 149.46 and 335.06 W/m2. Furthermore, a wind farm located in the selected location can generate about 59.7437, 90.4414, and 113.5075 GWh/y electricity by employing Gamesa G97/2000, GE Energy 2.75-120, and Senvion 3.4M140 wind turbines, respectively. Also, these wind farms can save approximately 22,110.80, 17,617.63, and 11,637.84 tons of CO2 emissions annually.


2015 ◽  
Vol 785 ◽  
pp. 621-626
Author(s):  
R. Shamsipour ◽  
M. Fadaeenejad ◽  
M.A.M. Radzi

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.


2019 ◽  
Vol 44 (3) ◽  
pp. 253-265
Author(s):  
Mohammadreza Mohammadpour Penchah ◽  
Hossein Malakooti

Currently, in order to reduce the time and cost of wind measurement masts installation, numerical meteorological models are being utilized to simulate the regional wind climate. In this study, the Weather Research and Forecasting model was applied to assess the wind power potential in the East of Iran. After evaluating the model, the wind mean speed and the power map were prepared based on a 5-year simulation (2011–2015). The results indicated that there were high wind-energy-potential areas bordering Iran and Afghanistan. The model illuminated that Khaf city had a satisfactory wind potential, reasonably corresponding with the observations. The model error was greater for the low wind speeds. With regard to the wind resource map of the area, only 3% of the study area falls within class 3 or higher, and therefore, can be considered conformative to the wind power generation.


Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4291
Author(s):  
Paxis Marques João Roque ◽  
Shyama Pada Chowdhury ◽  
Zhongjie Huan

District of Namaacha in Maputo Province of Mozambique presents a high wind potential, with an average wind speed of around 7.5 m/s and huge open fields that are favourable to the installation of wind farms. However, in order to make better use of the wind potential, it is necessary to evaluate the operating conditions of the turbines and guide the independent power producers (IPPs) on how to efficiently use wind power. The investigation of the wind farm operating conditions is justified by the fact that the implementation of wind power systems is quite expensive, and therefore, it is imperative to find alternatives to reduce power losses and improve energy production. Taking into account the power needs in Mozambique, this project applied hybrid optimisation of multiple energy resources (HOMER) to size the capacity of the wind farm and the number of turbines that guarantee an adequate supply of power. Moreover, considering the topographic conditions of the site and the operational parameters of the turbines, the system advisor model (SAM) was applied to evaluate the performance of the Vestas V82-1.65 horizontal axis turbines and the system’s power output as a result of the wake effect. For any wind farm, it is evident that wind turbines’ wake effects significantly reduce the performance of wind farms. The paper seeks to design and examine the proper layout for practical placements of wind generators. Firstly, a survey on the Namaacha’s electricity demand was carried out in order to obtain the district’s daily load profile required to size the wind farm’s capacity. Secondly, with the previous knowledge that the operation of wind farms is affected by wake losses, different wake effect models applied by SAM were examined and the Eddy–Viscosity model was selected to perform the analysis. Three distinct layouts result from SAM optimisation, and the best one is recommended for wind turbines installation for maximising wind to energy generation. Although it is understood that the wake effect occurs on any wind farm, it is observed that wake losses can be minimised through the proper design of the wind generators’ placement layout. Therefore, any wind farm project should, from its layout, examine the optimal wind farm arrangement, which will depend on the wind speed, wind direction, turbine hub height, and other topographical characteristics of the area. In that context, considering the topographic and climate features of Mozambique, the study brings novelty in the way wind farms should be placed in the district and wake losses minimised. The study is based on a real assumption that the project can be implemented in the district, and thus, considering the wind farm’s capacity, the district’s energy needs could be met. The optimal transversal and longitudinal distances between turbines recommended are 8Do and 10Do, respectively, arranged according to layout 1, with wake losses of about 1.7%, land utilisation of about 6.46 Km2, and power output estimated at 71.844 GWh per year.


2015 ◽  
Vol 2 (1) ◽  
pp. 25-36
Author(s):  
Otieno Fredrick Onyango ◽  
Sibomana Gaston ◽  
Elie Kabende ◽  
Felix Nkunda ◽  
Jared Hera Ndeda

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.


2015 ◽  
Vol 12 (4) ◽  
pp. 369-374 ◽  
Author(s):  
Afsin Gungor

A recent study conducted to determine the potential of wind power in Nigde which used 35 year wind data, has shown that global warming may also affect the potential of wind power negatively. The wind data were collected on 10 min time intervals at 10 m mast height. The missing data were 3.9%. When the results are closely examined it is observed that the potential of wind power has decreased dramatically throughout the years. The 35 yearly data has shown a decrease of wind power density from 48.14 W/m2 to 13.25 W/m2. These results are of extreme importance because of various reasons given below. The first problem we may see is that it is possible to observe an area which was once regarded as a highly suitable region for wind energy generation is now not as sustainable as it was assumed to be. Thus it may stand as a hidden but great risk for certain wind farm investments. Therefore, the calculation of wind power potential is a really serious matter to deal with. Moreover, if the loss of the wind power potential is observed consistently and continually as a result of global warming, the only reasonable solution to this problem may be the relocation of the whole power-plant.


2021 ◽  
Vol 66 (1) ◽  
pp. 100-108
Author(s):  
Cristian Paul Chioncel ◽  
Nicoleta Gillich ◽  
Gelu-Ovidiu Tirian

Once the wind data is measured, the values are processed, based on statistic approach, as accurately as possible, to provide a clear over-view of the locations wind potential, being the basis of any wind farm project, representing the go or no-go in further subsequent design steps. The probability density distributions are derived from time-series data, identifying the associated distributional parameters. The wind energy potential of the locations is studied based on the Rayleigh and Weibull models, implemented with the help of Excel computations, and representing tools, to understand the wind characteristics. Based on the statistical analysis of wind conditions presented here, the results of current study can be used to make a sustainable energy yield for any location.


2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Nkongho Ayuketang Arreyndip ◽  
Ebobenow Joseph

The method of generalized extreme value family of distributions (Weibull, Gumbel, and Frechet) is employed for the first time to assess the wind energy potential of Debuncha, South-West Cameroon, and to study the variation of energy over the seasons on this site. The 29-year (1983–2013) average daily wind speed data over Debuncha due to missing values in the years 1992 and 1994 is gotten from NASA satellite data through the RETScreen software tool provided by CANMET Canada. The data is partitioned into min-monthly, mean-monthly, and max-monthly data and fitted using maximum likelihood method to the two-parameter Weibull, Gumbel, and Frechet distributions for the purpose of determining the best fit to be used for assessing the wind energy potential on this site. The respective shape and scale parameters are estimated. By making use of the P values of the Kolmogorov-Smirnov statistic (K-S) and the standard error (s.e) analysis, the results show that the Frechet distribution best fits the min-monthly, mean-monthly, and max-monthly data compared to the Weibull and Gumbel distributions. Wind speed distributions and wind power densities of both the wet and dry seasons are compared. The results show that the wind power density of the wet season was higher than in the dry season. The wind speeds at this site seem quite low; maximum wind speeds are listed as between 3.1 and 4.2 m/s, which is below the cut-in wind speed of many modern turbines (6–10 m/s). However, we recommend the installation of low cut-in wind turbines like the Savonius or Aircon (10 KW) for stand-alone low energy need.


2020 ◽  
Vol 12 (4) ◽  
pp. 1483
Author(s):  
Adam Juma Abdallah Gudo ◽  
Jinsong Deng ◽  
Marye Belete ◽  
Ghali Abdullahi Abubakar

Energy security is one of the challenging issues hindering developmental progress in developing countries. Wind power as a renewable energy source can play a significant role in poverty reduction if adequate information is provided. In this study, multi-approach technics were applied for a better understanding of the wind energy potential in Jubek State, South Sudan. Geographic Information System (GIS), remote sensing, and mathematical equations were applied in identifying suitable locations, potential power per unit area, wind farm layout, design of appropriate turbine size, and utilization of wind energy in both agricultural and domestic sectors. Wind speed, land use land cover, and digital elevation maps of the study area were processed in ArcGIS, MATLAB (Weibull distribution), and Minitab software. The results show that 17,331.4 km2 (94.64%) of the study area is appropriate for wind power generation, with wind density of about 3.65 W/m2 and installation capacity about 19,757.79 MW, resulting in an annual energy production of about 7269.29 GWh. With the proposed wind turbine, one ton of various crops and animal products require 1–4 and 2–20 turbines, respectively. Therefore, the step-by-step procedures followed in this study will contribute to poverty reduction through improving agricultural productivity and food quality.


Author(s):  
Watchara Saeheng ◽  
Piyanut Saengsikhiao ◽  
Juntakan Taweekun

Over the past decades, Wind energy is one of the alternative energy or renewable sources, which has been harvested to produce electricity. Our research aims to study the wind potential of the areas in the Rayong provinces of Thailand. Data from meteorological stations were collected every 10 minutes for of 3 years (2017-2019), with a measuring tower at 10-meter height above ground level (AGL). The annual average wind speeds were investigated in Rayong (2.02 m/s) with Weibull Probability Distribution Function (PDF). The annual average power density in Rayong regions was 13 W/m2. In all locations, wind direction was detected mainly from Southwest (SSW) and the yearly maximum wind power capacity is 94.376 MWh. The capacity factor of 21.5 % was noticed. With relatively low wind speed was noticed in Rayong provinces of Thailand, a small wind turbine installed at 30 meters would be recommended as a cost-effective way to convert wind power to electricity.


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