Abstarct: The aim of this thesis is to investigate the effect of clustering methods in the classifying the rotor angle swings in a multi-machine power system. In the clustering baxsed coherency methods, depending on the disturbance location in the power system, the power system classification can be performed with regards to the similar rotor angle swings. The large power systems consist of many number of buses, lines and synchronous generators, the dynamic studies of this system is time consuming and needs a lot of computations. Thus, the clustering methods are employed in this thesis to determine the coherent generators. In this thesis, different methods for identification of coherent generators baxsed on clustering methods are used. The unsupervised clustering methods are used in this thesis are hierarchical clustering, K-means clustering and fuzzy C-means clustering. The clustering methods used in this thesis, are baxsed on the similarity matrix. The coherent generators baxsed the three mentioned methods are used to identify coherent generator for 39 bus and 68-bus test systems. To validate the suggested method, the behavior of the equivalent generator for each cluster of coherent generators is obtained. The choose of one of the three clustering methods is dependent on the data matrix. In this thesis, for the two test systems, similar results are obtained.