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 Wind Energy Potential as a Power Generation Source: A Case Study of Eight Selected Locations in Northern Cyprus

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2697 ◽  
Author(s):  
Mohamad Alayat ◽  
Youssef Kassem ◽  
Hüseyin Çamur
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Actual Data ◽  
Small Scale ◽  
Energy Potential ◽  
Northern Cyprus ◽  
Mean Wind Speed ◽  
Wind Energy Potential

This paper presents a techno-economic assessment of the wind power potential for eight locations distributed over the Northern part of Cyprus. The wind speed data were collected from the meteorological department located in Lefkoşa, Northern Cyprus.Ten distribution models were used to analyze the wind speed characteristics and wind energy potential at the selected locations. The maximum-likelihood method was used for calculating the parameters of the distribution functions.The power law model is utilized to determine the mean wind speed at various heights. In addition, the wind power density for each location was estimated. Furthermore, the performances of different small-scale vertical axis 3–10 kW wind turbines were evaluated to find those that were suitable and efficient for power generation in the studied locations.The results showed that the annual mean wind speed in the regions is greater than 2 m/s at a height of 10 m. Moreover, it is indicated that Generalized Extreme Value distribution provided the best fit to the actual data for the regions of Lefkoşa, Ercan, Girne, Güzelyurt, and Dipkarpaz. However, the Log-Logistic, Weibull, and Gamma distributions gave a better fit to the actual data of Gazimağusa, YeniBoğaziçi, and Salamis, respectively. The Rayleigh distribution does not fit the actual data from all regions. Furthermore, the values of wind power densityat the areas studied ranged from 38.76 W/m2 to 134.29 W/m2 at a height of 50 m, which indicated that wind energy sources in these selected locations are classified as poor. Meanwhile, based on the wind analysis, small-scale wind turbine use can be suitable for generating electricity in the studied locations. Consequently, an Aeolos-V2 with a rating of 5 kW was found to be capable of producing the annual energy needs of an average household in Northern Cyprus.

scholarly journals Evaluation of the Technical Wind Energy Potential of Kisii Region Based on the Weibull and Rayleigh Distribution Models

2018 ◽  
Author(s):  
Laban N. Ongaki ◽  
Christopher M. Maghanga ◽  
Joash Kerongo
Keyword(s):  
Wind Speed ◽  
Rayleigh Distribution ◽  
Series Data ◽  
Energy Potential ◽  
Distribution Models ◽  
Wind Speeds ◽  
Average Wind ◽  
Mean Wind Speed ◽  
Wind Energy Potential

The research sought to investigate the long term characteristics of wind in the Kisii region (elevation 1710m above sea level, 0.68oS, 34.79o E). Wind speeds were analyzed and characterized on short term (per month for a year) and then simulated for long term (ten years) measured hourly series data of daily wind speeds at a height of 10m. The analysis included daily wind data which was grouped into discrete data and then calculated to represent; the mean wind speed, diurnal variations, daily variations as well as the monthly variations. The wind speed frequency distribution at the height 10 m was found to be 2.9ms-1 with a standard deviation of 1.5. Based on the two month’s data that was extracted from the AcuRite 01024 Wireless Weather Stations with 5-in-1 Weather Sensor experiments set at three sites in the region, averages of wind speeds at hub heights of 10m and 13m were calculated and found to be 1.7m/s, 2.0m/s for Ikobe station, 2.4m/s, 2.8m/s for Kisii University stations, and 1.3m/s, 1.6m/s for Nyamecheo station respectively. Then extrapolation was done to determine average wind speeds at heights (20m, 30m, 50m, and 70m) which were found to be 85.55W/m2, 181.75W/m2, 470.4W/m2 and 879.9W/m2 respectively. The wind speed data was used statistically to model a Weibull probability density function and used to determine the power density for Kisii region.

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 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.

scholarly journals Evaluation of Wind Energy Potential as a Power Generation Source in Chad

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Djamal Hissein Didane ◽  
Nurhayati Rosly ◽  
Mohd Fadhli Zulkafli ◽  
Syariful Syafiq Shamsudin
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Weather Condition ◽  
Small Scale ◽  
Range Analysis ◽  
Energy Potential ◽  
Prevailing Wind Direction ◽  
Wide Range ◽  
Mean Wind Speed

Long-term wind speed data for thirteen meteorological stations, measured over a five-year period, were statistically analyzed using the two-parameter Weibull distribution function. The purpose of this study is to reveal for the first time the wind power potentials in Chad and to provide a comprehensive wind map of the country. The results show that the values of the shape and scale parameters varied over a wide range. Analysis of the seasonal variations showed that higher wind speed values occur when the weather condition is generally dry and they drop considerably when the weather condition is wet. It was also observed that the wind speed increases as one moves from the southern zone to the Saharan zone. Although the wind power at each site varies significantly, however, the potentials of most of the sites were encouraging. Nevertheless, according to the PNNL classification system, they are favorable for small-scale applications only. A few stations in the middle of Sudanian and Sahel regions are found to be not feasible for wind energy generation due to their poor mean wind speed. The prevailing wind direction for both Saharan and Sahel regions is dominated by northeastern wind, while it diverged to different directions in the Sudanian zone.

scholarly journals Statistical Analysis of Wind Speed & Wind Direction in Tantan Province, Morocco

2019 ◽  
Vol 8 (4) ◽  
pp. 3955-3959
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Direction ◽  
Energy System ◽  
Average Frequency ◽  
Wind Rose ◽  
Data Set ◽  
Wind Speeds ◽  
Mean Wind Speed ◽  
The Mean

In this paper, a two-parameter Weibull statistical distribution is used to analyze the characteristics of the wind from the Saharan area, located in the Tantan province, Morocco, for 08 years at 10 m. During those 08 years (2009-2017) the frequency distribution of the wind speed, the wind direction, the mean wind speed, the shape and scale (k & c) Weibull parameters have been calculated for the province. The mean wind speed for the entire data set is 6.4 m/s. The parameters k & c are found as 1.9 and 2.52 m/s in relative order. The study also provides an analysis of the wind direction along with a wind rose chart for the province. The analysis suggests that the highest wind speeds that vary (vm = 5.1m/s; vmax = 18.5m/s) prevail between sectors 165-175 ° with an average frequency of 1.4% and lower wind speeds (vm = 2.5m/s; vmax = 9.7m/s) occur between sectors 245-255° with an average frequency of 0.6%. The results of this document help to understand the wind power potential of the province and serve as a source of wind power projects. From a perspective, the wind energy system is an alternative to the future of the Sahara province of Morocco.

The eastern-Iran wind-resource assessment through a planetary boundary layer simulation (2011–2015)

2019 ◽  
Vol 44 (3) ◽  
pp. 253-265
Author(s):  
Mohammadreza Mohammadpour Penchah ◽  
Hossein Malakooti
Keyword(s):  
Wind Power ◽  
Resource Assessment ◽  
Energy Potential ◽  
Wind Speeds ◽  
Eastern Iran ◽  
Wind Potential ◽  
East Of Iran ◽  
Wind Energy Potential ◽  
Low Wind Speeds ◽  
Wind Resource

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.

scholarly journals Performance of Wind Power Plant in West Kalimantan, Indonesia

2020 ◽  
Vol 6 (3) ◽  
pp. 301
Author(s):  
Rudi Kurnianto ◽  
Firsta Zukhrufiana Setiawati ◽  
Bomo Wibowo Sanjaya ◽  
Rudy Gianto ◽  
Seno Darmawan Panjaitan ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Meteorological Station ◽  
Correction Factors ◽  
Physical Sciences ◽  
Satellite Measurements ◽  
Energy Potential ◽  
West Kalimantan ◽  
Wind Speeds ◽  
Wind Energy Potential

The aim of this paper is threefold. First, to calculate the correction factors based on existing meteorological station positions and its extension according to the basic map of West Kalimantan. Second, to reanalysis, the wind speeds from satellite measurements of national oceanic, and atmospheric administration (NOAA) and physical sciences division (PSD) in West Kalimantan by calculating its resultant and applying correction factor to create a map of wind power potential in West Kalimantan. Last, to apply the highest corrected wind speeds using HOMER simulation to know the wind energy potential. Conclusions have extracted the characteristics of wind speed variability over West Kalimantan, and HOMER simulation results of the highest wind speed.

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.

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