This work investigates inferential procedures for a reliability framework in which several components, each possessing its own strength profile, are exposed to a common stress. The analysis is carried out under a progressive censoring structure and assumes that both stress and strength follow the Burr distribution. Within this setting, the study develops and compares multiple estimation techniques for the multi–component stress–strength reliability index. The methodological contributions include the derivation of maximum likelihood estimators, Bayesian estimators obtained under squared–error loss, and highest posterior density (HPD) credible intervals. To evaluate the numerical behavior of these estimators, a large–scale Monte Carlo experiment is conducted, focusing on accuracy, stability, and interval performance. To highlight the practical relevance of the proposed methodology, the techniques are applied to satellite–based wind–speed observations provided by NASA. Two Iranian regions—Manjil and Ardabil—serve as case studies for assessing the feasibility of wind–energy development. The empirical findings indicate that Manjil exhibits significantly stronger and more consistent wind patterns, making it a more promising candidate for large–scale wind–power installations. These results underscore the necessity of further feasibility assessments in Manjil to support future renewable–energy planning and sustainability initiatives.