Abstract
An advanced fuzzy C-mean(FCM) algorithm for the efficient regional clustering of multi-nodes interconnected systems is presented in this paper. Owing to physical characteristics of the interconnected systems, nodes or points in the interconnected systems have their own information indicating the network-related characteristics of the system. However, classification for the whole system into distinct several subsystems based on a similarity measure is typically needed for the efficient operation of the whole system. In this paper, therefore, a new regional clustering algorithm for interconnected systems based on the modified FCM is proposed. Moreover, the regional information on the system are taken into account in order to properly address the geometric mis-clustering problem such as grouping geometrically distant nodes with similar measures into a common cluster. We have presented that the proposed algorithm has produced proper classification for the interconnected system and the results are demonstrated in the example of IEEE 39-bus interconnected electricity system.
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Lee, SH., Kim, JH., Jang, SH., Park, JB., Jeon, YH., Sohn, SY. (2007). An Advanced Fuzzy C-Mean Algorithm for Regional Clustering of Interconnected Systems. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_64
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DOI: https://doi.org/10.1007/978-3-540-71701-0_64
Publisher Name: Springer, Berlin, Heidelberg
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