Comparison of numerical methods in estimating Weibull parameters to install a sustainable wind farm in mount Bamboutos, Cameroon

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marinette Jeutho Gouajio ◽  
Pascalin Tiam Kapen ◽  
David Yemele
Keyword(s):  
Numerical Methods ◽  
Wind Power ◽  
Power Density ◽  
Wind Direction ◽  
Wind Farm ◽  
Content Type ◽  
Frequency Distributions ◽  
Weibull Parameters ◽  
Wind Power Density ◽  
North East

Purpose The purpose of this paper is to evaluate the wind energy potential of Mount Bamboutos in Cameroon by comparing nine numerical methods in determining Weibull parameters for the installation of a sustainable wind farm. Design/methodology/approach By using statistical analysis, the analysis of shape and scale parameters, the estimation of the available wind power density and wind direction frequency distributions, the objective of this paper is to compare nine numerical methods in estimating Weibull parameters for the installation of a sustainable wind farm in Mount Bamboutos, Cameroon. Findings The results suggested that the minimum and maximum values of the standard deviation occurred in the months of May and November 2016, respectively. The graphical method appeared to be the most effective method with the maximum value of variance and minimum values of chi-square and RMSE. The scale factor parameter values indicated that Mount Bamboutos hills were a potential site for electricity generation. The analysis of wind power density showed that it reached the maximum and minimum values in February and September, respectively. The wind direction frequency distributions showed that the prevailing wind directions were North-East. Originality/value The wind energy potential of Mount Bamboutos in Cameroon was performed by using nine numerical methods. Therefore, it could be effective to have a prediction model for the wind speed profile. The analysis of wind power density showed that it reached the maximum and minimum values in February and September, respectively. The wind direction frequency distributions showed that the prevailing wind directions were North-East.

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

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.

scholarly journals Short-term prediction of wind power density using convolutional LSTM network

2021 ◽  
Vol 49 (3) ◽  
pp. 653-663
Author(s):  
Deepak Gupta ◽  
Vikas Kumar ◽  
Ishan Ayus ◽  
M. Vasudevan ◽  
N. Natarajan
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Power Density ◽  
Tamil Nadu ◽  
Wind Farm ◽  
Mean Squared Error ◽  
Short Term ◽  
Wind Power Density ◽  
Operation Conditions ◽  
Lstm Network

Efficient extraction of renewable energy from wind depends on the reliable estimation of wind characteristics and optimization of wind farm installation and operation conditions. There exists uncertainty in the prediction of wind energy tapping potential based on the variability in wind behavior. Thus the estimation of wind power density based on empirical models demand subsequent data processing to ensure accuracy and reliability in energy computations. Present study analyses the reliability of the ANN-based machine learning approach in predicting wind power density for five stations (Chennai, Coimbatore, Madurai, Salem, and Tirunelveli) in the state of Tamil Nadu, India using five different non-linear models. The selected models such as Convolutional Neural Network (CNN), Dense Neural Network (DNN), Recurrent Neural Network (RNN), Bidirectional Long Short Term Memory (LSTM) Network, and linear regression are employed for comparing the data for a period from Jan 1980 to May 2018. Based on the results, it was found that the performance of (1->Conv1D|2->LSTM|1-dense) is better than the other models in estimating wind power density with minimum error values (based on mean absolute error and root mean squared error).

scholarly journals Potential of Wind Energy Development for Water Abstraction Systems in Developing Country Context: A Case of Teso Sub-Region of Uganda

2018 ◽  
Author(s):  
James Tondo Kasozi ◽  
Nicholas Kiggundu ◽  
Joshua Wanyama ◽  
Noble Banadda
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
High Water ◽  
Frequency Distributions ◽  
Water Abstraction ◽  
Wind Power Density ◽  
Wind Speeds ◽  
Dry Seasons ◽  
Wind Energy Development

Wind energy powered pumps could be an alternative to conventional fuel powered pumps for water abstraction because they rely on a free energy and they are environmentally friendly. The objective of this study was to assess the potential of wind energy to operate water abstraction systems in Teso sub-region of Uganda for livestock watering Daily mean wind speeds recorded at a height of 10 m for a period of ten years (2005–2015) were collected from Amuria and Soroti Meteorological stations in the study area. Data were analyzed using Weibull distribution to evaluate the annual wind speed frequency distributions and consequently assess their potential for water abstraction. The results indicated that warmer months (January, February and March) have higher mean wind speeds than the cold months (August, September and October). High wind speeds in the dry seasons corresponded to the periods of high water demand. The highest shape parameter (k) of 3.07 was registered in 2009 and scale parameter (c) of 3.78 in 2012. The highest wind power density of 43 W/m2 was obtained the year 2012 while the lowest wind power density of 15.47 W/m2 was obtained for Soroti district in the year 2009. The maximum power extractable in Amuria in 2012 was 324 W/m2 which is potentially enough for water abstraction. Maximum discharges of 1.86 m3/s and 1.52 m3/s were obtained for Amuria and Soroti districts respectively at mean wind speeds of 5 m/s. Therefore, Teso sub region winds have potential for water abstraction and Amuria district better sites for livestock watering using wind energy.

scholarly journals Estimating Weibull Parameters for Wind Energy Applications using Seven Numerical Methods: Case studies of three costal sites in West Africa

2020 ◽  
Vol 9 (2) ◽  
pp. 217-226
Author(s):  
Agbassou Guenoukpati ◽  
Adekunlé Akim Salami ◽  
Mawugno Koffi Kodjo ◽  
Kossi Napo
Keyword(s):  
Standard Deviation ◽  
Wind Speed ◽  
Numerical Methods ◽  
West Africa ◽  
Wind Power ◽  
Power Density ◽  
Empirical Method ◽  
Wind Power Density ◽  
Mean Wind Speed ◽  
Speed Standard Deviation

In this study, the effectiveness of seven numerical methods is evaluated to determine the shape (K) and scale (C) parameters of Weibull distribution function for the purpose of calculating the wind speed characteristics and wind power density. The selected methods are graphical method (GPM), empirical method of Justus (EMJ), empirical method of Lysen (EML), energy pattern factor method (EPFM), maximum likelihood method (MLM) moment method (MOM) and the proposed. Hybrid method (HM) derived from EPFM and EMJ. The purpose is to identify the most appropriate method for computing the mean wind speed, wind speed standard deviation and wind power density for different costal locations in West Africa. Three costal sites (Lomé, Accra and Cotonou) are selected. The input data was collected, from January 2004 to December 2015 for Lomé site, from January 2009 to December 2015 for Accra site and from January 2009 to December 2012 for Cotonou. The results indicate that the precision of the computed mean wind speed, wind speed standard deviation and wind power density values change when different parameters estimation methods are used. Five of them which are EMJ, EML, EPF, MOM, ML, and HM method present very good accuracy while GPM shows weak ability for all three sites. ©2020. CBIORE-IJRED. All rights reserved

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