To assess the potential of wind energy in a specific area, statistical distribution functions are commonly used to characterize wind speed distributions. The selection of an appropriate wind speed model is crucial in minimizing wind power estimation errors. In this paper, we propose a novel method that utilizes the T-X family of continuous distributions to generate two new wind speed distribution functions, which have not been previously explored in the wind energy literature. These two statistical distributions, namely the Weibull-three parameters-log-logistic (WE3-LL3) and log-logistic-three parameters-Weibull (LL3-WE3) are compared with four other probability density functions (PDFs) to analyze wind speed data collected in Tabriz, Iran. The parameters of the considered distributions are estimated using maximum likelihood estimators with the Nelder-Mead numerical method. The suitability of the proposed distributions for the actual wind speed data is evaluated based on criteria such as root mean square errors, coefficient of determination, Kolmogorov-Smirnov test, and chi-square test. The analysis results indicate that the LL3-WE3 distribution demonstrates generally superior performance in capturing seasonal and annual wind speed data, except for summer, while the WE3-LL3 distribution exhibits the best fit for summer. It is also observed that both the LL3-WE3 and WE3-LL3 distributions effectively describe wind speed data in terms of the wind power density error criterion. Overall, the LL3-WE3 and WE3-LL3 models offer a highly accurate fit compared to other PDFs for estimating wind energy potential.

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