Assessment of Wind Energy Potential Using Weibull Distribution Function as Wind Power Plant in Medan, North Sumatra

2016 ◽  
Author(s):  
I Musirin
Keyword(s):  
Distribution Function ◽  
Power Plant ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Wind Power ◽  
Wind Power Plant ◽  
Energy Potential ◽  
Weibull Distribution Function ◽  
Wind Energy Potential ◽  
North Sumatra

scholarly journals Determining wind energy resources in Turkmenistan

2021 ◽  
Vol 22 (6) ◽  
pp. 143-154
Author(s):  
K. A. Saryyev
Keyword(s):  
Power Plant ◽  
Wind Energy ◽  
Wind Power ◽  
Social Issues ◽  
Research Work ◽  
Energy Resources ◽  
Wind Power Plant ◽  
Energy Potential ◽  
Wind Energy Potential ◽  
Wind Energy Resources

THE PURPOSE. Studying the complex issues associated with providing consumers who are away from the central power supply system with environmentally safe renewable energy source, i.e. conversion of wind energy into electrical energy and uninterrupted supply of electricity to consumers. In this area, one of the complex issues is to determine the wind energy resources in the area where it is planned to install a wind farm.The article deals with the determination of wind energy resources in Turkmenistan. Using databases obtained from meteorological stations over several years, calculations of wind energy resources by region are carried out. This will quickly and in detail analyze the wind energy potential in a particular area to assess the choice of capacity of the projected wind turbines, with the aim of ensuring sustainable development of the region and the reliability of electricity supply.METHODS. In order to estimate the wind energy resources in the area it is necessary to determine the average wind speed per year. In this research work the average annual wind speeds obtained from meteorological stations installed in different regions of Turkmenistan were used. These data were compared with the actual data obtained from the 2 kW wind power plant installed at the research site of the State Energy institute of Turkmenistan and the relevant graphs were built. The results of the obtained data can be used for optimal selection of wind power plant locations and assessment of wind energy resources of the region. RESULTS.As a result of the calculations, wind energy reserves in the region and for the regions of Turkmenistan were determined, as well as the optimal locations of wind farms.Using the technical characteristics of a 2kW wind power plant, the average annual electricity output of a wind power plant of different capacity is determined.With the correct definition of wind energy potential, there is an opportunity to solve energy, economic, environmental and social issues of the country. And also there is an opportunity to mitigate climate change on the basis of wind energy installations, and their resources, environmental benefits, goals and objectives on the scientific methodological basis in the field of wind energy for implementation of state programs for energy conservation in Turkmenistan and energy supply of the region.CONCLUSIONS. On the basis of the carried out scientific work the wind energy resources and technical potentials of wind power plants in the territory of Turkmenistan were determined and the database for compiling the wind energy cadastre was created.

scholarly journals Optimum Method for Determining Weibull Distribution Parameters Used in Wind Energy Estimation

2020 ◽  
pp. 635-648
Author(s):  
Ulku Erisoglu ◽  
Nil Aras ◽  
Hasan Donat Yildizay
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Wind Power ◽  
Estimation Method ◽  
Energy Potential ◽  
Average Wind ◽  
Distribution Parameters ◽  
Wind Energy Potential ◽  
Two Parameter

One of the well-known methods for the determination of wind energy potential is the two-parameter Weibull distribution. It is clear that the success of the Weibull distribution for wind energy applications depends on the estimation of the parameters which can be determined by using various numerical methods. In the present study, Monte Carlo simulation method is performed by using six parameters estimation method that is used in the estimation of Weibull distribution parameters such as Maximum Likelihood Estimation (MLE), Least Squares Method (LSM), Method of Moments (MOM), Method of Logarithmic Moments (MLM), Percentile Method (PM), and L-Moment Method (LM), and is compared to Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). In this study, the wind energy potential of the Meşelik region in Eskişehir was modeled with two-parameter Weibull distribution. The average wind speed (m/s) data, which are gathered in 10-minute intervals from the measuring device installed 10 meters about the ground in Meşelik Campus of Eskişehir Osmangazi University, is used. As a result of the simulation study, it has been determined that MLE is the best parameter estimation method for two-parameter Weibull distribution in large sample sizes, and LM has the closest performance to MLE. The wind speed (m/h) data of the region has been successfully modeled with two-parameter Weibull distribution and the highest average wind power density has been obtained in July as 49.38295 (W/m2) while the lowest average wind power density has been obtained in October as 19.30044 (W/m2).

scholarly journals Technical Assessment of Wind Energy Potentiality in Malaysia Using Weibull Distribution Function

2021 ◽  
Vol 86 (1) ◽  
pp. 1-13
Author(s):  
Abdul Rashid Shoib ◽  
Djamal Hissein Didane ◽  
Akmal Nizam Mohammed ◽  
Kamil Abdullah ◽  
Mas Fawzi Mohd Ali
Keyword(s):  
Distribution Function ◽  
Wind Speed ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Wind Power ◽  
Maximum Energy ◽  
Small Scale ◽  
Weibull Distribution Function ◽  
Average Value ◽  
Wind Speeds

In this paper, an assessment of the wind characteristics and wind power potentials in three different stations (Chuping, Kuantan and Melaka) in Malaysia has been analyzed at 80 m height. The assessment technique was based on the two-parameter Weibull distribution function over three recent consecutive years (2018-2020) while aiming to establish the potentiality of the wind as a source of power generation in these sites. The results demonstrate that the monthly highest mean wind speeds were 4.42 m/s, 2.96 m/s and 2.17 m/s at Melaka, Kuantan and Chuping, respectively. The highest most probable wind speed was 4.70 m/s and the wind speed carrying maximum energy was 1.74 m/ both speeds occurred at Maleka in 2019. The yearly highest Weibull shape parameter was 1.69 and the scale parameter was 2.96 m/s. Among the three stations, Melaka has shown the highest wind power potentials with an average value of 26.10 W/m2 followed by Kuantan with 12.71 W/m2 and Chuping with 6.80 W/m2 wind power density. The corresponding wind energy densities were 595.58 kWh/m2/year, 111.37 kWh/m2/year and 228.65 kWh/m2/year for Chuping, Kuantan and Melaka station, respectively. The prevailing wind directions are northeast at both Kuantan and Melaka station, west and southwest direction at Chuping station. It is therefore concluded that the potentiality of the wind power of the sites covered in the present study is only feasible for small-scale power generations.

Calculation of Weibull Distribution parameters at low wind speed and performance analysis

2021 ◽  
pp. 1-24
Author(s):  
Yusuf Alper Kaplan
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Energy Potential ◽  
Average Wind Speed ◽  
Statistical Indicator ◽  
Low Wind Speed ◽  
Distribution Parameters ◽  
Wind Energy Potential ◽  
And Performance

In this study, the compatibility of the real wind energy potential to the estimated wind energy potential by Weibull Distribution Function (WDF) of a region with low average wind speed potential was examined. The main purpose of this study is to examine the performance of six different methods used to find the coefficients of the WDF and to determine the best performing method for selected region. In this study seven-year hourly wind speed data obtained from the general directorate of meteorology of this region was used. The root mean square error (RMSE) statistical indicator was used to compare the efficiency of all used methods. Another main purpose of this study is to observe the how the performance of the used methods changes over the years. The obtained results showed that the performances of the used methods showed slight changes over the years, but when evaluated in general, it was observed that all method showed acceptable performance. Based on the obtained results, when the seven-year data is evaluated in this selected region, it can be said that the MM method shows the best performance.

Sign in / Sign up

Export Citation Format

Share Document