Identifying generator coherency with respect to slow oscillatory modes has numerous power system use cases including dynamic model reduction, dynamic security analysis, or system integrity protection schemes (e.g., power system islanding). Despite their popularity in both research and industry, classic eigenvector-based slow coherency techniques may not always return accurate results. The multiple past endeavors to improve their accuracy often lack a solid mathematical foundation. Motivated by these deficiencies, we propose an alternative consistent approach to generator slow coherency. Firstly, a new approach is introduced to accurately detect slow coherent generators by effectively minimizing generic normalized cuts. As a by-product, the new approach can also guide the choice of the number of slow coherent groups. Secondly, it is shown that the combination of the the proposed slow coherency approach and an enhanced version of the inertial generator aggregation method allows to produce accurate dynamic equivalents even if the selected number of generator groups is relatively low.

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This paper presents an integrated approach to parti-5 tion similarity graphs, the task that arises in various contexts in 6 power system studies. The approach is based on orthogonal trans-7 formation of row-normalized eigenvectors obtained from spectral 8 clustering to closely fit the axes of the canonical coordinate system. 9 We select the number of clusters as the number of eigenvectors 10 that allows the best alignment with the canonical coordinate axes, 11 which is a more informative approach than the popular spectral 12 eigengap heuristic. We show a link between the two relevant meth-13 ods from the literature and on their basis construct a robust and 14 time-efficient algorithm for eigenvector alignment. Furthermore, 15 a graph partitioning algorithm based on the use of aligned eigen-16 vector columns is proposed, and its efficiency is evaluated by com-17 parison with three other methods. Finally, the proposed integrated 18 approach is applied to the adaptive reconfiguration of secondary 19 voltage control helping to achieve demonstrable improvements in 20 control performance. 21 Index Terms-Power network partitioning, spectral clustering, 22 number of clusters, adaptive network zone division.23

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