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In this paper, several clustering algorithms are investigated in order to group together wind parks with close statistical behavior. Here, the proposed approach is practically founded on a fast incremental algorithm validated by a normalized principal component analysis combined with a k-means process. Both methods are practically based on the definition of a Pearson correlation coefficient. The advantage of such a clustering methodology is mainly perceptible in large-scale electrical systems with increased wind penetration. Indeed, it allows to group together highly correlated wind parks into the same cluster and to integrate them in a realistic way into a nonsequential Monte Carlo adequacy evaluation process. Here, the implemented clustering methodology is applied to 94 wind sites located in Occidental Europe. Then, in order to point out the efficiency of this clustering methodology that is afterwards combined with an original wind speed sampling process, an adequacy study is applied to the Roy Billinton Test System in the particular case of two wind clusters.