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This study presents an approach for developing the dynamic equivalent model of an active distribution network (ADN), consisting of several micro‐grids, for frequency stability studies. The proposed grey‐box equivalent model relies on Prony analysis to establish stop time and load damping as the required modelling parameters. Support vector clustering (SVC) and grouping procedure are employed for aggregation and order‐reduction of ADN. This significantly decreases the sensitivity of the estimated parameters to operating point changes which, in turn, guarantees the model robustness. This is done through representing the SVC output, that is, clusters, by cluster substitutes. The final ADN dynamic equivalent model is represented by several groups, in which their mutual interactions are taken into account by a new developed mathematical‐based criterion. Simulation results reveal that the proposed model is robust which could successfully take into account the continuous and discontinuous uncertainties. [ABSTRACT FROM AUTHOR]

This study investigates the application of support vector clustering (SVC) for the direct identification of coherent synchronous generators in large interconnected multi‐machine power systems. The clustering is based on coherency measure, which indicates the degree of coherency between any pair of generators. The proposed SVC algorithm processes the coherency measure matrix that is formulated using the generator rotor measurements to cluster the coherent generators. The proposed approach is demonstrated on IEEE 10 generator 39‐bus system and an equivalent 35 generators, 246‐bus system of practical Indian southern grid. The effect of number of data samples and fault locations are also examined for determining the accuracy of the proposed approach. An extended comparison with other clustering techniques is also included, to show the effectiveness of the proposed approach in grouping the data into coherent groups of generators. This effectiveness of the coherent clusters obtained with the proposed approach is compared in terms of a set of clustering validity indicators and in terms of statistical assessment that is based on the coherency degree of a generator pair. [ABSTRACT FROM AUTHOR]