Wind Farming Feasibility Assessment in 16 Locations of Nepal

The paper assesses the feasibility of wind farming at the 16 sites scattered in different ecological regions of Nepal. The wind speed, the hourly and seasonal variation of wind, the wind-rose, the wind turbulence rate, the wind power density, the Weibull probability distribution and the frequency of the wind speed above cut in speed were computed. The average wind speed at all the sites was found to be higher during the dry season from March to May. The wind speed of the modern turbine for power generation at eight sites was found to be above cut-in speed. However, the wind power density was found to be good only at the two sites and fairly good at the six sites. More than 50 % time of a year at these eight sites had over 3.5 m/s wind speed. However, the turbulence rate at all the studied sites was found to be above the acceptance range of 25 %. Among the study sites, Kagbeni, Thini, Jumla, Ramechhap, Vorleni, Patan west, Hansapur and Baddanda were found to be technically feasible sites for wind energy generation in Nepal.

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Evaluation of Wind Potential for the Generation of Electricity in Aliero, Kebbi State

Asian Journal of Research and Reviews in Physics ◽

10.9734/ajr2p/2020/v3i130110 ◽

2020 ◽

pp. 1-7

Author(s):

A. A. Yahaya ◽

I. M. Bello ◽

N. Mudassir ◽

I. Mohammed ◽

M. I. Mukhtar

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Wind Power ◽

Power Density ◽

Horizontal Wind ◽

Average Wind Speed ◽

Wind Power Density ◽

North East ◽

Average Wind ◽

Wind Potential

One of the major developments in the technology today is the wind turbine that generates electricity and feed it directly to the grid which is used in many part of the world. The main purpose of this work is to determine the wind potential for electricity generation in Aliero, Kebbi state. Five years Data (2014-2018) was collected from the metrological weather station (Campell Scientific Model), the equipment installed at Kebbi State University of Science And Technology Aliero The data was converted to monthly and annual averages, and compared with the threshold average wind speed values that can only generate electricity in both vertical and horizontal wind turbines. The highest average wind speed 2.81 m/s was obtained in the month of January and the minimum average wind speed of 1.20 m/s in the month of October. Mean annual wind speed measured in the study area shows that there has been an increase in the wind speed from 2014 which peaked in 2015 and followed by sudden decrease to a minimum seasonal value in the year 2016. The highest wind direction is obtained from the North North-East (NNE) direction. From the results of wind power density it shows that we have highest wind power density in month of January and December with 0.8635 w/ m2 and 0.8295 w/ m2 respectively, while lowest wind power density in the month of October and September with 0.6780 w/ m2 and 0.6575 w/ m2 respectively. Result of the type Wind Turbine to be selected in the study area shows that the site is not viable for power generation using a horizontal wind turbine but the vertical wind turbine will be suitable for the generation of electricity.

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Assessment of Wind Energy Potential In Zwara, Libya

Solar Energy and Sustainable Development ◽

10.51646/jsesd.v8i2.21 ◽

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.

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The super-turbine wind power conversion paradox: using machine learning to reduce errors caused by Jensen's inequality

Wind Energy Science ◽

10.5194/wes-4-343-2019 ◽

2019 ◽

Vol 4 (2) ◽

pp. 343-353 ◽

Cited By ~ 1

Author(s):

Tyler C. McCandless ◽

Sue Ellen Haupt

Keyword(s):

Machine Learning ◽

Standard Deviation ◽

Wind Speed ◽

Wind Power ◽

Power Conversion ◽

Jensen’S Inequality ◽

Jensen's Inequality ◽

Average Wind Speed ◽

Wind Speeds ◽

Average Wind

Abstract. Wind power is a variable generation resource and therefore requires accurate forecasts to enable integration into the electric grid. Generally, the wind speed is forecast for a wind plant and the forecasted wind speed is converted to power to provide an estimate of the expected generating capacity of the plant. The average wind speed forecast for the plant is a function of the underlying meteorological phenomena being predicted; however, the wind speed for each turbine at the farm is also a function of the local terrain and the array orientation. Conversion algorithms that assume an average wind speed for the plant, i.e., the super-turbine power conversion, assume that the effects of the local terrain and array orientation are insignificant in producing variability in the wind speeds across the turbines at the farm. Here, we quantify the differences in converting wind speed to power at the turbine level compared with a super-turbine power conversion for a hypothetical wind farm of 100 2 MW turbines as well as from empirical data. The simulations with simulated turbines show a maximum difference of approximately 3 % at 11 m s−1 with a 1 m s−1 standard deviation of wind speeds and 8 % at 11 m s−1 with a 2 m s−1 standard deviation of wind speeds as a consequence of Jensen's inequality. The empirical analysis shows similar results with mean differences between converted wind speed to power and measured power of approximately 68 kW per 2 MW turbine. However, using a random forest machine learning method to convert to power reduces the error in the wind speed to power conversion when given the predictors that quantify the differences due to Jensen's inequality. These significant differences can lead to wind power forecasters overestimating the wind generation when utilizing a super-turbine power conversion for high wind speeds, and indicate that power conversion is more accurately done at the turbine level if no other compensatory mechanism is used to account for Jensen's inequality.

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Assessment of wind energy potential and characteristics in Qatar for clean electricity generation

Wind Engineering ◽

10.1177/0309524x211043855 ◽

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.

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

Energy Exploration & Exploitation ◽

10.1177/0144598719875276 ◽

2019 ◽

Vol 38 (1) ◽

pp. 175-200 ◽

Cited By ~ 7

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.

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

Energies ◽

10.3390/en13071846 ◽

2020 ◽

Vol 13 (7) ◽

pp. 1846 ◽

Cited By ~ 2

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.

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Wind Power Generating Potential at the University of North Texas Campus

Volume 6: Energy, Parts A and B ◽

10.1115/imece2012-89082 ◽

2012 ◽

Author(s):

Rambod Rayegan ◽

Yong X. Tao ◽

Frank Y. Fang

Keyword(s):

Wind Speed ◽

Wind Power ◽

Power Density ◽

Wind Turbines ◽

North Texas ◽

University Of North Texas ◽

Average Wind Speed ◽

Wind Speed Data ◽

Weather Stations ◽

The University

This study utilizes two sets of wind speed data at 3 m above the ground surface level retrieved from two on-campus weather stations to study the wind power generating potential at the University of North Texas Campus. Weather stations have been installed approximately 5 miles away from each other. The mean wind speed data of 10 minute intervals in a one-year period from February 1st 2011 to January 31st 2012 has been adopted and analyzed. The numerical values of the dimensionless Weibull shape parameter (k) and Weibull scale parameter (c) have been determined. Monthly average wind speed and standard deviation, power generation, and power density at the sensor level for both locations has been discussed. Lower values of wind speed were found during summer months and higher during spring months. The results show that the wind power density in the area is fair enough to be considered as a renewable power source for the University. Thereafter annual energy production by using two wind turbines with nominal capacities of 100 and 3.5 kW for both weather stations has been studied. Initial costs of using each turbine to maintain power demands of selected buildings have been compared. In order to utilize wind energy, it is recommended to install highly efficient wind turbines for electricity supply of campus buildings with lower power demands. Using grant monies to maintain the initial costs of the installation of wind turbines make them economically more desirable. Since wind power potential is low during summer, PV panels as proper supplements to the power generating system are suggested.

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Wind potential estimation and proposed energy production in Southern Punjab using Weibull probability density function and surface measured data

Energy Exploration & Exploitation ◽

10.1177/0144598720920748 ◽

2020 ◽

pp. 014459872092074 ◽

Cited By ~ 2

Author(s):

Muhammad Sumair ◽

Tauseef Aized ◽

Syed Asad Raza Gardezi ◽

Syed Ubaid Ur Rehman ◽

Syed Muhammad Sohail Rehman

Keyword(s):

Wind Speed ◽

Energy Density ◽

Wind Energy ◽

Wind Power ◽

Power Density ◽

Energy Production ◽

Economic Assessment ◽

Weibull Model ◽

Wind Power Density ◽

Wind Potential

Current work focusses on the wind potential assessment in South Punjab. Eleven locations from South Punjab have been analyzed using two-parameter Weibull model (with Energy Pattern Factor Method to estimate Weibull parameters) and five years (2014–2018) hourly wind data measured at 50 m height and collected from Pakistan Meteorological Department. Techno-economic analysis of energy production using six different turbine models was carried out with the purpose of presenting a clear picture about the importance of turbine selection at particular location. The analysis showed that Rahim Yar Khan carries the highest wind speed, highest wind power density, and wind energy density with values 4.40 ms−1, 77.2 W/m2 and 677.76 kWh/m2/year, respectively. On the other extreme, Bahawalnagar observes the least wind speed i.e. 3.60 ms−1 while Layyah observes the minimum wind power density and wind energy density as 38.96 W/m2 and 352.24 kWh/m2/year, respectively. According to National Renewable Energy Laboratory standards, wind potential ranging from 0 to 200 W/m2 is considered poor. Economic assessment was carried out to find feasibility of the location for energy harvesting. Finally, Polar diagrams drawn to show the optimum wind blowing directions shows that optimum wind direction in the region is southwest.

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Statistical analysis of wind speed and wind power potential of Port Elizabeth using Weibull parameters

Journal of Energy in Southern Africa ◽

10.17159/2413-3051/2012/v23i2a3160 ◽

2012 ◽

Vol 23 (2) ◽

pp. 30-38 ◽

Cited By ~ 21

Author(s):

Temitope R Ayodele ◽

Adisa A. Jimoh ◽

Josiah L. Munda ◽

John T. Agee

Keyword(s):

Wind Speed ◽

Wind Power ◽

South African ◽

Shape Parameter ◽

Scale Parameter ◽

Average Wind Speed ◽

Weibull Parameters ◽

Average Wind ◽

Port Elizabeth ◽

Power Project

This paper analyses wind speed characteristics and wind power potential of Port Elizabeth using statistical Weibull parameters. A measured 5–minute time series average wind speed over a period of 5 years (2005 - 2009) was obtained from the South African Weather Service (SAWS). The results show that the shape parameter (k) ranges from 1.319 in April 2006 to 2.107 in November 2009, while the scale parameter (c) varies from 3.983m/s in May 2008 to 7.390 in November 2009.The average wind power density is highest during Spring (September–October), 256.505W/m2 and lowest during Autumn (April-May), 152.381W/m2. This paper is relevant to a decision-making process on significant investment in a wind power project.

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