scholarly journals Generalized Extreme Value Distribution Models for the Assessment of Seasonal Wind Energy Potential of Debuncha, Cameroon

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Nkongho Ayuketang Arreyndip ◽  
Ebobenow Joseph
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Extreme Value ◽  
Likelihood Method ◽  
Generalized Extreme Value ◽  
Energy Potential ◽  
Monthly Data ◽  
Wind Speeds ◽  
Wind Energy Potential

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.

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 Climate Change Induced Uncertainty of Wind Energy Potential for the Azov and Black Seas Coastal Zone

2020 ◽  
Author(s):  
V. P. Evstigneev ◽  
◽  
N. A. Lemeshko ◽  
V. A. Naumova ◽  
M. P. Evstigneev ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Black Sea ◽  
Operating Time ◽  
Energy Output ◽  
Energy Potential ◽  
Wind Energy Potential ◽  
Wind Climate

The paper deals with assessing an impact of wind climate change on the wind energy potential of the Azov and Black Sea coast region. A lower estimate of operating time for wind power installation and a potential annual energy output for the region are given for the case of Vestas V117-4.2MW. Calculation has been performed of a long-term mean wind speed for two adjacent climatic periods (1954–1983 and 1984–2013) based on data from meteorological stations of the Black and Azov Sea region. The results show a decrease in wind speed at all meteorological stations except for Novorossiysk. The wind climate change is confirmed by comparing two adjoined 30-year periods and by estimating linear trends of the mean annual wind speed for the period 1954–2013, which are negative and significant for almost all meteorological stations in the region (α = 1 %). The trend values were estimated by the nonparametric method of robust linear smoothing using the Theil – Sen function. In the present study, the uncertainty of wind energy resource induced by a gradual wind climate change is estimated for perspective planning of this branch of energy sector. Despite the observed trends in the wind regime, average wind speeds in the Azov and Black Sea region are sufficient for planning the location of wind power plants.

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 and characteristics in Qatar for clean electricity generation

2021 ◽  
pp. 0309524X2110438
Author(s):  
Carlos Méndez ◽  
Yusuf Bicer
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Rose ◽  
Energy Potential ◽  
Wind Power Density ◽  
Wind Speeds ◽  
Wind Energy Potential

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.

scholarly journals Assessment of wind energy potential across varying topographical features of Tamil Nadu, India

2019 ◽  
Vol 38 (1) ◽  
pp. 175-200 ◽  
Author(s):  
Shafiqur Rehman ◽  
Narayanan Natarajan ◽  
Mangottiri Vasudevan ◽  
Luai M Alhems
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Tamil Nadu ◽  
High Energy ◽  
Energy Potential ◽  
Annual Average ◽  
Wind Power Density ◽  
Wind Energy Potential

Wind energy is one of the abundant, cheap and fast-growing renewable energy sources whose intensive extraction potential is still in immature stage in India. This study aims at the determination and evaluation of wind energy potential of three cities located at different elevations in the state of Tamil Nadu, India. The historical records of wind speed, direction, temperature and pressure were collected for three South Indian cities, namely Chennai, Erode and Coimbatore over a period of 38 years (1980-2017). The mean wind power density was observed to be highest at Chennai (129 W/m2) and lowest at Erode (76 W/m2) and the corresponding mean energy content was highest for Chennai (1129 kWh/m2/year) and lowest at Erode (666 kWh/m2/year). Considering the events of high energy-carrying winds at Chennai, Erode and Coimbatore, maximum wind power density were estimated to be 185 W/m2, 190 W/m2 and 234 W/m2, respectively. The annual average net energy yield and annual average net capacity factor were selected as the representative parameters for expressing strategic wind energy potential at geographically distinct locations having significant variation in wind speed distribution. Based on the analysis, Chennai is found to be the most suitable site for wind energy production followed by Coimbatore and Erode.

scholarly journals An Assessment of Wind Energy Potential for the Three Topographic Regions of Eritrea

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1846 ◽  
Author(s):  
Teklebrhan Negash ◽  
Erik Möllerström ◽  
Fredric Ottermo
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Coastal Region ◽  
Energy Potential ◽  
Wind Power Density ◽  
Prevailing Wind Direction ◽  
Central Highland ◽  
Wind Energy Potential

This paper presents the wind energy potential and wind characteristics for 25 wind sites in Eritrea, based on wind data from the years 2000–2005. The studied sites are distributed all over Eritrea, but can roughly be divided into three regions: coastal region, western lowlands, and central highlands. The coastal region sites have the highest potential for wind power. An uncertainty, due to extrapolating the wind speed from the 10-m measurements, should be noted. The year to year variations are typically small and, for the sites deemed as suitable for wind power, the seasonal variations are most prominent in the coastal region with a peak during the period November–March. Moreover, Weibull parameters, prevailing wind direction, and wind power density recalculated for 100 m above ground are presented for all 25 sites. Comparing the results to values from the web-based, large-scale dataset, the Global Wind Atlas (GWA), both mean wind speed and wind power density are typically higher for the measurements. The difference is especially large for the more complex-terrain central highland sites where GWA results are also likely to be more uncertain. The result of this study can be used to make preliminary assessments on possible power production potential at the given sites.

scholarly journals Assessment of Wind Energy Potential In Zwara, Libya

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
Alhassan A. Teyabeen ◽  
Fathi R. Akkari ◽  
Ali E. Jwaid ◽  
Ashraf Zaghwan ◽  
Rehab Abodelah
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Ground Level ◽  
Energy Potential ◽  
Average Wind Speed ◽  
Wind Power Density ◽  
Wind Energy Potential ◽  
Wind Resource

To assess the wind energy potential at any site, the wind power density should be estimated; it evaluates the wind resource and indicates the amount of available wind energy. The purpose of this study is to estimate the monthly and annual wind power density based on the Weibull distribution using wind speed data collected in Zwara, Libya during 2007. The wind date are measured at the three hub heights of 10m, 30m, and 50m above ground level, and recorded every 10 minutes. The analysis showed that the annual average wind speed are 4.51, 5.86, 6.26 m/s for the respective mentioned heights. The average annual wind power densities at the mentioned heights were 113.71, 204.19, 243.48 , respectively.

scholarly journals Assessment of wind energy potential in East Azarbaijan province of Iran (Case Stady: Sahand station)

2015 ◽  
Vol 17 (2) ◽  
pp. 418-425
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Fossil Fuels ◽  
Ground Level ◽  
Energy Potential ◽  
Wind Speeds ◽  
Mean Wind Speed ◽  
Wind Energy Potential ◽  
Seasonal Wind

<p>Today&#39;s world requires a change in how the use of different types of energy. With declining reserves of fossil fuels for renewable energies is of course the best alternative. Among the renewable energy from the wind can be considered one of the best forms of energy can be introduced. Accordingly, most countries are trying to identify areas with potential to benefit from this resource.</p> <p>The aim of this study was to assess the potential wind power in Sahand station of Iran country. Hourly measured long term wind speed data of Sahand during the period of 2000-2013 have been statistically analyzed. In this study the wind speed frequency distribution of location was found by using Weibull distribution function. The wind energy potential of the location has been studied based on the Weibull mode. The results of this study show that mean wind speed measured at 10 m above ground level is determined as 5.16 m/s for the studied period. This speed increases by, respectively, 34.78 % and 41.21 %, when it is extrapolated to 40 and 60 m hub height.</p> <div> <p>Long term seasonal wind speeds were found to be relatively higher during the period from January to September. At the other hand, higher wind speeds were observed between the period between 06:00 and 18:00 in the day. These periods feet well with annual and daily periods of maximum demand of electricity, respectively.&nbsp;</p> </div> <p>&nbsp;</p>

scholarly journals Techno-Economic Investigation of Wind Energy Potential in Selected Sites with Uncertainty Factors

2021 ◽  
Vol 13 (4) ◽  
pp. 2182
Author(s):  
Varadharajan Sankaralingam Sriraja Balaguru ◽  
Nesamony Jothi Swaroopan ◽  
Kannadasan Raju ◽  
Mohammed H. Alsharif ◽  
Mun-Kyeom Kim
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Tamil Nadu ◽  
Series Data ◽  
Energy Potential ◽  
Data Set ◽  
Uncertainty Factors ◽  
Wind Energy Potential

This work demonstrates a techno-economical assessment of wind energy potential for four passes of Tamil Nadu (Aralvaimozhi, Shencottah, Palghat, and Cumbum) with uncertainty factors. First, a potential assessment was carried out with time-series data, and the Weibull parameters, such as c (scale) and k (shape), were determined using the modern-era retrospective analysis for research and applications (MEERA) data set. Using these parameters, the mean speed, most probable speed, power density, maximum energy-carrying speed of wind power were determined. From the analysis, it was observed that all four passes had better wind parameters; notably, the Aralvaimozhi pass attained a better range of about 6.563 m/s (mean wind speed), 226 W/m2 (wind power density), 6.403 m/s (most probable wind speed), and 8.699 m/s (max wind speed). Further, uncertainty factors, such as the probability of exceedance (PoE), wind shear co-efficient (WSC), surface roughness, and wake loss effect (WLE), were evaluated. The value of PoE was found to be within the bound for all the locations, i.e., below 15%. In addition, the ranged of WSC showed a good trend between 0.05 and 0.5. Moreover, the surface length of the passes was evaluated and recorded to be 0.0024 m with a 73% energy index. Further, output power, annual energy production (AEP), capacity factor (CF), and cost of wind energy of all four passes were computed using different wind turbine ratings in two cases, i.e., with and without WLE. It was observed that there was a huge profit in loss from all the four locations due to WLE that was estimated to be Rupees (Rs.) 10.07 crores without considering interest components and Rs. 13.66 crores with interest component at a 10% annual rate of interest.

scholarly journals An Assessment of Onshore and Offshore Wind Energy Potential in India Using Moth Flame Optimization

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3063 ◽  
Author(s):  
Krishnamoorthy R ◽  
Udhayakumar K ◽  
Kannadasan Raju ◽  
Rajvikram Madurai Elavarasan ◽  
Lucian Mihet-Popa
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Likelihood Method ◽  
Wind Speeds ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
South West Monsoon ◽  
Wind Distribution ◽  
Wind Resource

Wind energy is one of the supremely renewable energy sources and has been widely established worldwide. Due to strong seasonal variations in the wind resource, accurate predictions of wind resource assessment and appropriate wind speed distribution models (for any location) are the significant facets for planning and commissioning wind farms. In this work, the wind characteristics and wind potential assessment of onshore, offshore, and nearshore locations of India—particularly Kayathar in Tamilnadu, the Gulf of Khambhat, and Jafrabad in Gujarat—are statistically analyzed with wind distribution methods. Further, the resource assessments are carried out using Weibull, Rayleigh, gamma, Nakagami, generalized extreme value (GEV), lognormal, inverse Gaussian, Rician, Birnbaum–Sandras, and Bimodal–Weibull distribution methods. Additionally, the advent of artificial intelligence and soft computing techniques with the moth flame optimization (MFO) method leads to superior results in solving complex problems and parameter estimations. The data analytics are carried out in the MATLAB platform, with in-house coding developed for MFO parameters estimated through optimization and other wind distribution parameters using the maximum likelihood method. The observed outcomes show that the MFO method performed well on parameter estimation. Correspondingly, wind power generation was shown to peak at the South West Monsoon periods from June to September, with mean wind speeds ranging from 9 to 12 m/s. Furthermore, the wind speed distribution method of mixed Weibull, Nakagami, and Rician methods performed well in calculating potential assessments for the targeted locations. Likewise, the Gulf of Khambhat (offshore) area has steady wind speeds ranging from 7 to 10 m/s with less turbulence intensity and the highest wind power density of 431 watts/m2. The proposed optimization method proves its potential for accurate assessment of Indian wind conditions in selected locations.

Sign in / Sign up

Export Citation Format

Share Document