# Estimating Weibull Parameters Using Mabchour's Method (MMab) for Wind Power at RAWA City, Iraq
## 3 Results and Discussions
It was noticed that when using the Mabchour (MMab) method, higher results were obtained, while it was found that values of \(k\) and \(c\) are close to each other. Furthermore, the values of the Weibell measurement coefficient had values that were very close to the measured values of average wind speed. The value of the correlation coefficient (\(R^{2}\) = 0.9971) between the Weibull parameters of the scale and the shape parameters were also calculated. In order to obtain high accuracy, the value of the correlation coefficient should be close to 1. The minimum value of the coefficient of variation (\(COV\)) for turbulence intensity that occurred during the months of July over three years were (27.84, 26.25 and 28.94), corresponding to (2017, 2018 and 2019), respectively, at 10 m elevation when the wind speeds were at their maximum. While the values of \(COV\) during the same months but at 50 m high were found to be (27.80, 26.23 and 28.93), corresponding to (2017, 2018 and 2019) respectively, when the wind speeds were at their maximum. The highest error of wind power density between the measured data and Weibull distribution (\(P_{W}\)) was found to be 4.48% at an elevation of 50 m. It is clear from the obtained results that the turbulence is relatively low at a height of 10 m and less at an elevation of 50 m. Therefore, the winds become more stable at a height above ground level, and this fact led to the lower value of \(COV\). Figure 5 shows the variation of the mean \(COV\) per hour during the selected period (2017-2019). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Height** & **Jan.** & **Feb.** & **Mar.** & **Apr.** & **May** & **Jun.** & **Jul.** & **Aug.** & **Sep.** & **Oct.** & **Nov.** & **Dec.** & **Yearly Mean** \\ \hline N(h) & 744.0 & 672.0 & 744.0 & 720.0 & 744.0 & 720.0 & 744.0 & 744.0 & 720.0 & 744.0 & 720.0 & 744.0 & 8760 \\ \hline \(v\) mean & 3.91 & 3.5 & 4.23 & 3.88 & 4.16 & 4.93 & 5.54 & 5.09 & 4.11 & 3.04 & 3.13 & 3.59 & 4.09 \\ \hline \(v\) max. & 13.87 & 9.76 & 14.2 & 10.89 & 9.93 & 13.78 & 9.47 & 10.17 & 8.47 & 8.01 & 9.45 & 8.62 & 10.5 \\ \hline \(v\) min. & 0.08 & 0.2 & 0.17 & 0.16 & 0.14 & 0.2 & 0.12 & 0.29 & 0.77 & 0.99 & 0.09 & 0.09 & 0.27 \\ \hline \(\sigma\) & 2.32 & 1.88 & 2.47 & 1.89 & 2.04 & 1.81 & 1.6 & 2.17 & 1.52 & 1.4 & 1.57 & 1.72 & 1.86 \\ \hline \(P_{M}\) & 36.61 & 26.26 & 69.06 & 35.77 & 44.09 & 73.39 & 104.1 & 126.4 & 42.52 & 25.81 & 18.78 & 28.33 & 52.29 \\ \hline \(COV\) & 59.39 & 53.75 & 58.34 & 48.82 & 48.97 & 36.66 & 28.94 & 42.79 & 36.94 & 45.93 & 50.28 & 48.0 & 46.56 \\ \hline \end{tabular}
\end{table} 
Table 7: The monthly of mean wind speed in (m/s) and (\(COV\)) coefficient of variation at 10 m height during the period 2019. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Height** & **Jan.** & **Feb.** & **Mar.** & **Apr.** & **May** & **Jun.** & **Jul.** & **Aug.** & **Sep.** & **Oct.** & **Nov.** & **Dec.** & **Yearly Mean** \\ \hline N(h) & 744.0 & 672.0 & 744.0 & 720.0 & 744.0 & 720.0 & 744.0 & 744.0 & 720.0 & 744.0 & 720.0 & 744.0 & 8760 \\ \hline \(v\) mean & 4.89 & 4.37 & 5.29 & 4.85 & 5.21 & 6.17 & 6.92 & 6.27 & 5.14 & 3.81 & 3.91 & 4.49 & 5.11 \\ \hline \(v\) max. & 17.33 & 12.2 & 17.75 & 13.61 & 12.41 & 17.22 & 11.83 & 12.71 & 10.58 & 10.01 & 11.81 & 10.77 & 13.18 \\ \hline \(v\) min. & 0.1 & 0.25 & 0.21 & 0.2 & 0.17 & 0.25 & 0.15 & 0 & 0.96 & 0.11 & 0.11 & 0.11 & 0.21 \\ \hline \(\sigma\) & 2.9 & 2.35 & 3.09 & 2.36 & 2.55 & 2.26 & 2.0 & 2.64 & 1.89 & 1.75 & 1.97 & 2.15 & 2.33 \\ \hline \(P_{M}\) & 71.61 & 51.11 & 90.67 & 69.87 & 86.62 & 143.8 & 202.9 & 150.9 & 83.17 & 33.87 & 54.75 & 55.44 & 91.22 \\ \hline \(COV\) (\%) & 59.27 & 53.7 & 58.38 & 48.82 & 48.95 & 36.68 & 28.93 & 42.21 & 36.96 & 45.95 & 50.33 & 48.03 & 46.52 \\ \hline \end{tabular}
\end{table} 
Table 8: The monthly of mean wind speed in (m/s) and (\(COV\)) coefficient of variation at 50 m height during the period 2019. Equation (14) was used to calculate the error in the estimation of wind energy density.