Comparative analysis of three numerical methods for estimating the onshore wind power in a coastal area

• Main Authors: Nedaei, Mojtaba, Ataei, Abtin, Adaramola, Muyiwa Samuel, Hajiseyed Mirzahosseini, Alireza, Khalaji Assadi, Morteza, Assareh, Ehsanolah
• Format: Article
• Published: Taylor&Francis 2016
• Online Access: http://eprints.utp.edu.my/12017/1/Comparative%20analysis%20of%20three%20numerical%20methods.pdf; http://eprints.utp.edu.my/12017/

Although there are many studies concerning the estimation of wind potential in different locations of Iran, an adequate evaluation of wind power for onshore locations of this country has not been investigated yet. The purpose of this research is to evaluate the potential of onshore wind in the south of Iran along the Gulf of Oman by comparing three Weibull numerical methods. In the first step, it became clear that the maximum likelihood method (MLM) compared to other methods represented the actual wind data with the lowest error rate and therefore it was selected as an accurate statistical distribution to model the wind speed of the investigated location. Using the MLM, the estimation of wind speed characteristics as well as the techno-economic evaluation of different wind turbines has been investigated. It was concluded that both technically and economically the studied area does not have sufficient wind power for the development of large-scale or grid-connected wind turbines. However, it may have enough power for non-grid-connected mechanical applications, such as wind generators for water pumping. Therefore, in the last part of this paper, an investigation of water pumping potential of the studied site by using adequate and modern wind turbines with lower start-up wind speed was recommended for future studies. Furthermore, it was suggested that the current methodology used in this research could be employed and extended in future studies to evaluate the wind potential of all onshore locations of Iran including onshore locations along the Persian Gulf and Caspian Sea.