This article shows how the Double K-means methodology can be used for the proper determination of grey classes over a set of electrical frequency deviation measurements. The Double K-means methodology to determine grey classes has the quality of being automated which allows the execution of its algorithm simultaneously with the input of measurements (online) or with stored measurements (offline). It is a contribution to science by the researcher as it is useful for the analysis of large amounts of oscillating data such as the electrical frequency deviation indicator and other Power Quality parameters using the Grey clustering and Entropy Weight methodology, which allows decision making or qualification of the behavior, service or phenomenon.