Wind Statistical Analysis and the Number of Generation Hours for Different Wind Turbines at Three Lakes in Iraq

The use of wind as an energy source is becoming popular because it is non-polluting and renewable. There is a pressing need in Iraq to develop site-based technology on wind energy, which can be used for optimal design of wind turbines and wind farming. The main objective of this research is to analyze the wind data statistics for one year period of January to December 2012 at three lakes in Iraq; Tharthar, Habbaniyah, and Razzazah. In the other hand, data recorded at 10m are estimated at 30m and 50m above ground level. Statistical analysis of these data is achieved using Weibull distribution function; the analysis included different wind turbine machines (500 kW, 950 kW, and 1500 kW) in order to assess the number of hours electricity generation from those turbines. The results show that Tharthar Lake gives more generation hours than the other lakes depending on the site characteristics and on the turbine type. Keywords: Wind speed, Weibull parameters estimation, Generation hours, lakes, Iraq

Estimate the probability density function of maximum temperature for the Middle East

Pollution is one reasons for increase temperature which leads to increase the heat waves which have large socioeconomic and healthy impacts on Middle East. By using monthly daily mean of maximum temperature (C°) at height (2m) covered middle east as a grid of (1581) points for selected months (March, April, May) represent spring and (Jun, July, August) represent Summer for the period 1979 to2018, from the ECMWF, model ERA-interim. Many PDFs have been proposed in recent past, but in present study Logistic, Rayleigh and Gamma distribution are used to describe the characteristics of maximum temperature. This paper attempts to determine the best fitted probability distribution of maximum temperature. To check the accuracy of the predicted data using theoretical probability distributions the goodness of fit criteria Z-test used in this paper. According to the goodness-of-fit criteria and from the graphical comparisons it can be said that Logistic distribution provides the best fit for the observed monthly daily mean of maximum temperature data