Determination of the best Weibull methods for wind power assessment in the southern region of Turkey

Abstract

In this study, wind energy potential in the South of Turkey was investigated statistically by using the Turkish State Meteorological Service's hourly wind speed data between 2009 and 2013. The wind data used in this study were gathered from the Meteorology Station in Hatay and Osmaniye. In this study, different numerical methods were analysed and their performances were compared for effectiveness in determining the shape ‘k’ and scale ‘c’ parameters of the Weibull distribution function for two different regions. Six different methods: namely, graphical method, empirical method, maximum likelihood method, energy trend method, energy pattern method and moment method were used to estimate the Weibull parameters. The following statistical indicators were used for comparing the efficiency of all the methods used: the root mean square error, analysis of variance (R2) and mean percentage error. Wind power densities were also calculated for all numerical methods used in this study. The power density is the key issue for the suitable use of wind energy. The calculated power densities for all methods used were compared with wind power density derived from measured wind data for two regions.

Wind Energy Potential Assessment in Selected Regions in Northern Cyprus Based on Weibull Distribution Function

“…As mentioned in the previous section, Weibull distribution is widely and successfully utilized to evaluate wind speed characteristics when analysing the wind energy potential of the interested region. A function that gives the probability distributions of the wind speeds can be expressed with the following equation (Sinden, 2007;Dahmouni et al, 2011;Kamau et al, 2010;Kaplan, 2017;Ahmed, 2018):…”

“…where, f(v) represents the probability of observing wind speed, if v<0, then f(v) = 0, vis the wind speed, c is the scale parameter in m/s (c>1)and k is the shape factor of distribution (k>0). The shape of the best fit curve of the distribution can vary depending on the values of k and c. Moreover, the cumulative distribution function for Weibull distribution is expressed with the relation given below (Sinden, 2007;Dahmouni et al, 2011;Kamau et al, 2010;Kaplan, 2017;Ahmed, 2018);…”

Determination of Weibull parameters with using Standard Deviation Method and performance comparison at different locations

“…As Rayleigh distribution has a single parameter, it is less flexible compared to that of Weibull's, however, the estimation of its parameters is much easier. Wind data analysis of a region is a process in which the performance of that region is predicted by utilizing the values that have been previously measured and obtained accordingly [19,20].…”

Reliability Modeling of Complex Electronic System Based on Weibull Distribution

“…The system failure generally follows a regular pattern and will go through three periods: early failure period, accidental failure period, and loss failure period [4]. The early failure period is due to defects and quality problems in the internal modules and components of the system.…”