The Effect of Missing Wind Speed Data on Wind Power Estimation

2007 ◽  
pp. 107-114 ◽  
Author(s):  
Fatih Onur Hocaog̃lu ◽  
Mehmet Kurban
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Power Estimation ◽  
Wind Speed Data

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.

scholarly journals A Statistical Analysis and Datamining Approach for Wind Speed Predication

2014 ◽  
Vol 14 (2) ◽  
pp. 5464-5478
Author(s):  
Mahesh K ◽  
Dr M V Vijayakumar ◽  
Gangadharaiah. Y.H .
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farms ◽  
Predictive Performance ◽  
Power Industry ◽  
New Paradigm ◽  
Prolog Program ◽  
Wind Speed Data ◽  
Wind Power Industry ◽  
Intelligent Technology

The wind power industry has seen an unprecedented growth in last few years. The surge in orders for wind turbines has resulted in a producers market. This market imbalance, the relative immaturity of the wind industry, and rapid developments in data processing technology have created an opportunity to improve the performance of wind farms and change misconceptions surrounding their operations. This research offers a new paradigm for the wind power industry, data-driven modeling. Each wind Mast generates extensive data for many parameters, registered as frequently as every minute. As the predictive performance approach is novel to wind industry, it is essential to establish a viable research road map. This paper proposes a data-mining-based methodology for long term wind forecasting (ANN), which is suitable to deal with large real databases. The paper includes a case study based on a real database of five years of wind speed data for a site and discusses results of wind power density was determined by using the Weibull and Rayleigh probability density functions. Wind speed predicted using wind speed data with Datamining methodology using intelligent technology as Artificial Neural Networks (ANN) and a PROLOG program designed to calculate the monthly mean wind speed.

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.

Mapping wind power density for Zimbabwe: a suitable Weibull-parameter calculation method

2014 ◽  
Vol 25 (4) ◽  
pp. 37-47 ◽  
Author(s):  
Tawanda Hove ◽  
Luxmore Madiye ◽  
Downmore Musademba
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Power Density ◽  
Graphical Method ◽  
Rayleigh Distribution ◽  
Wind Data ◽  
Wind Speed Data ◽  
Wind Power Density ◽  
Scale Parameters ◽  
Wind Energy Systems

The two-parameter Weibull probability distribution function is versatile for modelling wind speed frequency distribution and for estimating the energy delivery potential of wind energy systems if its shape and scale parameters, k and c, are correctly determined from wind records. In this study, different methods for determining Weibull k and c from wind speed measurements are reviewed and applied at four sample meteorological stations in Zimbabwe. The appropriateness of each method in modelling the wind data is appraised by its accuracy in predicting the power density using relative deviation and normalised root mean square error. From the methods considered, the graphical method proved to imitate the wind data most closely followed by the standard deviation method. The Rayleigh distribution (k=2 is also generated and compared with the wind speed data. The Weibull parameters were calculated by the graphical method for fourteen stations at which hourly wind speed data was available. These values were then used, with the assistance of appropriate boundary layer models, in the mapping of a wind power density map at 50m hub height for Zimbabwe.

Sign in / Sign up

Export Citation Format

Share Document