scholarly journals Influence of the Random Data Sampling in Estimation of Wind Speed Resource: Case Study

2021 ◽  
Vol 11 (1) ◽  
pp. 133-143
Author(s):  
Adekunlé Akim Salami ◽  
Seydou Ouedraogo ◽  
Koffi Mawugno Kodjoa ◽  
Ayité Sénah Akoda Ajavona
Keyword(s):  
Wind Speed ◽  
Power Density ◽  
Random Sampling ◽  
Estimation Accuracy ◽  
Percentage Error ◽  
Data Sampling ◽  
Wind Speed Data ◽  
Wind Speeds ◽  
Study Results ◽  
Wind Potential

In this study, statistical analysis is performed in order to characterize wind speeds distribution according to different samples randomly drawn from wind speed data collected. The purpose of this study is to assess how random sampling influences the estimation quality of the shape (k) and scale (c) parameters of a Weibull distribution function. Five stations were chosen in West Africa for the study, namely: Accra Kotoka, Cotonou Cadjehoun, Kano Mallam Aminu, Lomé Tokoin and Ouagadougou airport. We used the energy factor method (EPF) to compute shape and scale parameters. Statistical indicators used to assess estimation accuracy are the root mean square error (RMSE) and relative percentage error (RPE). Study results show that good accuracy in Weibull parameters and power density estimation is obtained with sampled wind speed data of 30% for Accra, 20% for Cotonou, 80% for Kano, 20% for Lomé, and 20% for Ouagadougou site. This study showed that for wind potential assessing at a site, wind speed data random sampling is sufficient to calculate wind power density. This is very useful in wind energy exploitation development.

scholarly journals Wind speed modeling based on measurement data to predict future wind speed with modified Rayleigh model

2021 ◽  
Vol 12 (3) ◽  
pp. 1823
Author(s):  
Suwarno Suwarno ◽  
Rohana Rohana
Keyword(s):  
Wind Speed ◽  
Measurement Data ◽  
Rayleigh Distribution ◽  
Scale Factor ◽  
Percentage Error ◽  
Monthly Data ◽  
Wind Speeds ◽  
Wind Characteristics ◽  
One Year ◽  
The Many

The development of modeling wind speed plays a very important in helping to obtain the actual wind speed data for the benefit of the power plant planning in the future. The wind speed in this paper is obtained from a PCE-FWS 20 type measuring instrument with a duration of 30 minutes which is accumulated into monthly data for one year (2019). Despite the many wind speed modeling that has been done by researchers. Modeling wind speeds proposed in this study were obtained from the modified Rayleigh distribution. In this study, the Rayleigh scale factor (<em>C<sub>r</sub></em>) and modified Rayleigh scale factor (<em>C<sub>m</sub></em>) were calculated. The observed wind speed is compared with the predicted wind characteristics. The data fit test used correlation coefficient (R<sup>2</sup>), root means square error (RMSE), and mean absolute percentage error (MAPE). The results of the proposed modified Rayleigh model provide very good results for users.

scholarly journals Frequency Distribution Model of Wind Speed Based on the Exponential Polynomial for Wind Farms

2019 ◽  
Vol 11 (3) ◽  
pp. 665 ◽  
Author(s):  
Lingzhi Wang ◽  
Jun Liu ◽  
Fucai Qian
Keyword(s):  
Wind Speed ◽  
Frequency Distribution ◽  
Least Squares Method ◽  
Wind Farms ◽  
Rayleigh Distribution ◽  
Distribution Model ◽  
Exponential Polynomial ◽  
Distribution Models ◽  
Wind Speed Data ◽  
Wind Speeds

This study introduces and analyses existing models of wind speed frequency distribution in wind farms, such as the Weibull distribution model, the Rayleigh distribution model, and the lognormal distribution model. Inspired by the shortcomings of these models, we propose a distribution model based on an exponential polynomial, which can describe the actual wind speed frequency distribution. The fitting error of other common distribution models is too large at zero or low wind speeds. The proposed model can solve this problem. The exponential polynomial distribution model can fit multimodal distribution wind speed data as well as unimodal distribution wind speed data. We used the linear-least-squares method to acquire the parameters for the distribution model. Finally, we carried out contrast simulation experiments to validate the effectiveness and advantages of the proposed distribution model.

Wind Power Generating Potential at the University of North Texas Campus

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.

Wind Potential Assessment for the Utilization from Wind Energy in the Household Level: A Case Study of Nakhonratchasima Thailand

2016 ◽  
Vol 835 ◽  
pp. 749-752
Author(s):  
Yuttachai Keawsuntia
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Alternative Energy ◽  
Energy Resource ◽  
Energy Utilization ◽  
Distribution Method ◽  
Household Level ◽  
Wind Speed Data ◽  
Wind Potential

Wind energy is an important alternative energy resource because of it clean, does not cause pollution and it can be used as replacement of a fossil fuel energy. Utilization of the wind energy, the wind speed data has to be analyzed to make sure before use it. In this article is to present the wind speed data analysis by using Weibull distribution method. Wind speed data from the meteorological station at Pakchong district, Nakhonratchasima province, Thailand was used as the case study. The results show that this area has wind speed about 2.5 to 3.5 m/s. The average wind power density was 17.513 W/m2 and the total wind energy was 153.9819 kW·hr/m2 per year. This wind potential of this area can be used for water pumping and electricity generating for use in a household.

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

2020 ◽  
pp. 014459872092074 ◽  
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.

Modifying Wind Speed Data of Manual Observation Sequence to Automatic Observation Sequence Using a PSO-Based Neural Network

2011 ◽  
Vol 187 ◽  
pp. 688-692
Author(s):  
Xia Xiao ◽  
Hong Chao Zuo ◽  
Wen Yu Zhang ◽  
Ju Jie Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Optimization Technique ◽  
Weather Data ◽  
Percentage Error ◽  
Observation Data ◽  
Data Sequence ◽  
Wind Speed Data ◽  
Observation Sequence ◽  
Particle Swarm Optimization Technique

Recently, manual observation sequence has been gradually replaced by automatic observation sequence. The difference between manual observation sequence and automatic observation sequence is somewhat inevitable. This challenges the the homogeneity and the continuity of historical weather data, and influences atmospheric researches and applications. Therefore, based on the understanding of the influence caused by the two observation sequences, how to modify the data sequence of manual observation to automatic observation sequence has become a problem. In this paper, a model, which is a neural network based on the particle swarm optimization technique (PSONN), is established to modify the wind speed data sequence from manual observation to automatic observation. The proposed model achieves 15.6% in mean absolute percentage error (MAPE) compared to manual observation data sequence. For wind speed, it could be a promising candidate for modifying manual observing data sequence to automatic observing data sequence.

scholarly journals The Investigation of Wind Energy Potential in Order to Estimate Power Plants in Baladeh Mazandaran, Iran

2012 ◽  
pp. 29-33
Author(s):  
S. Asghar Gholamian ◽  
S. Bagher Soltani ◽  
R. Ilka
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Power Plants ◽  
Weather Station ◽  
Energy Potential ◽  
Wind Power Density ◽  
Synoptic Weather ◽  
Wind Potential

First step for achieving wind energy is to locate points with appropriate wind power density in a country. Wind data which are recorded in a synoptic weather station, are the best way to study the wind potential of an area. In this paper wind speed period of Baladeh synoptic weather station is studied, since it has the maximum average of wind speed among 15 stations of the MAZANDARAN Province. Weibull factors k and c are calculated for 40 months from September 2006 to December 2009 and wind power density is determined based on these data. The total average of factors k and c for a height for 50 m are 1.442 m/s and 5.1256 respectively. By using the average of factors, wind power density in 50 m height will be 147.40 watt/m2 which is categorized as weak potential in wind class. However by monthly investigation it is shown that with a 50 m wind, this station can be put in medium class in hot months of the year.

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 Performance evaluation of Weibull function for wind data analysis in two selected locations in North-Western, Nigeria

2018 ◽  
Vol 6 (1) ◽  
pp. 18
Author(s):  
Boluwaji Olomiyesan
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Predictive Ability ◽  
Maximum Energy ◽  
Wind Data ◽  
Weibull Function ◽  
Wind Speed Data ◽  
Wind Speeds ◽  
The North ◽  
North Western

In this study, the predictive ability of two-parameter Weibull distribution function in analyzing wind speed data was assessed in two selected sites with different mean wind speeds in the North-Western region of Nigeria. Twenty-two years wind speed data spanning from 1984 to 2005 was used in the analysis. The data were obtained from the Nigerian Meteorological Agency (NIMET) in Lagos. The results of the analysis show that Weibull function is suitable for analyzing measured wind speed data and in predicting the wind-power density in both locations and that Weibull function is not discriminative between locations with high and low mean wind speeds in analyzing wind data. The annual mean wind speeds for the two sites (Sokoto and Yelwa) are 7.99 ms-1 and 2.59 ms-1 respectively, while the annual values of the most probable wind speed and the maximum, energy-carrying wind speeds are respectively:3.52 and 4.34 ms-1 for Yelwa and 8.33 and 9.02 ms-1 for Sokoto. The estimated annual wind power densities for Yelwa and Sokoto are respectively 36.91 and 359.96 Wm-2. Therefore, Sokoto has a better prospect for wind power generation.

scholarly journals Observation and Simulation of Wind Speed and Wind Power Density over Bac Lieu Region

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hiep Van Nguyen ◽  
Pham Xuan Thanh ◽  
Nguyen Duc Nam ◽  
Nguyen Xuan Anh ◽  
Pham Le Khuong ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Power Density ◽  
Bias Correction ◽  
Local Maximum ◽  
Observation Data ◽  
Wind Power Density ◽  
Wind Speeds ◽  
Diurnal Cycles ◽  
Southern Vietnam

In this study, the WRF (Weather Research and Forecasting) model was used to simulate and investigate diurnal and annual variations of wind speed and wind power density over Southern Vietnam at 2‐km horizontal resolution for two years (2016 and 2017). The model initial and boundary conditions are from the National Centers for Environmental Prediction (NCEP) Final Analyses (FNL). Observation data for two years at 20 m height at Bac Lieu station were used for model bias correction and investigating diurnal and annual variation of wind speeds. The results show that the WRF model overestimates wind speeds. After bias correction, the model reasonably well simulates wind speeds over the research area. Wind speed and wind power density show much higher values at levels of 50–200 m above ground levels than near ground (20 m) level and significantly higher near the coastal regions than inland. Wind speed has significant annual and diurnal cycles. Both annual and diurnal cycles of wind speeds were well simulated by the model. Wind speed is much stronger during daytime than at nighttime. Low-level wind speed reaches the maximum at about 14 LT to 15 LT when the vertical momentum mixing is highly active. Wind speeds over the eastern coastal region of Southern Vietnam are much stronger in winter than in summer due to two main reasons, including (1) stronger large-scale wind speed in winter than in summer and (2) funnel effect creating a local maximum wind speed over the nearshore ocean which then transports high-momentum air inland in winter.

Sign in / Sign up

Export Citation Format

Share Document