Mersing is one of the places that have the potential for wind power development in Malaysia. Researchers often suggest it as an ideal place for generating electricity from wind power. However, before a location is chosen, several factors need to be considered. By analyzing the location ahead of time, resource waste can be avoided and maximum profitability to various parties can be realized. For this study, the focus is to identify the distribution of the wind speed of Mersing and to determine the optimal average of wind speed. This study is critical because the wind speed data for any region has its distribution. It changes daily and by season. Moreover, no determination has been made regarding selecting the average wind speed used for wind studies. The wind speed data is averaged to 1, 10, 30, and 60 minutes and used to find the optimal wind speed average. This study used Kolmogorov-Smirnov and Chi-Square as the goodness of fit. The finding shows that the wind speed distribution in Mersing varies according to the time average used and the best fit distribution is Gen. Gamma. In contrast, the optimal average wind speed is 10 minutes due to the highest similarity results with 1-minute data. These affect the reliability of the finding, accuracy of the estimation and decisions made. Therefore, the implementation of this study is significant so that the wind distribution in a particular area is more accurate.
In this paper, estimation of system reliability of the multi-components in stress-strength model R(s,k) is considered, when the stress and strength are independent random variables and follows the Exponentiated Weibull Distribution (EWD) with known first shape parameter θ and, the second shape parameter α is unknown using different estimation methods. Comparisons among the proposed estimators through Monte Carlo simulation technique were made depend on mean squared error (MSE) criteria