Normalized Euclid Distance-Based Network Time-Domain Design Method and Its Application in Short-Pulse Differentiator

Chen, Hongtai; Wang, Jialin; Li, Shunli; Zhao, Hongxin; Yin, Xiaoxing

doi:10.1109/lmwc.2022.3144671

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Fuzzy C-means Clustering Algorithm1

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2023

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“…The technical process of fuzzy clustering uses fuzzy logic to assign data points to various groups and offers a useful way to separate overlapping clusters. Applications with continuous or overlapping profiles benefit from fuzzy clustering more [17]. An objective formulation that should be minimized in the FCM method is as follows:…”

Section: Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%

Optimal reconfiguration of distribution network based on deep fuzzy c-means clustering algorithm

Yang

Zhao

et al. 2023

J. Phys.: Conf. Ser.

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This study examines the best distribution network reconfiguration to enhance distribution network safety and reduce active power loss. Distribution networks can be reconfigured using a deep fuzzy C-means (FCM) clustering method with the objective of the least amount of active power loss. By creating a novel framework, the size of the neural network is lowered by modifying the number of neurons in input layer. With the simulations tested on IEEE 33-bus distribution network, the outcomes of traditional approaches, such as the Branch-and-Cut and the switching algorithms, are contrasted with the simulation results. The comparative results clearly show advantages to employing the suggested framework for distribution network reconfiguration, such as a quick turnaround time far quicker than the alternatives. These characteristics demonstrate how well the suggested paradigm may be applied to distribution network real-time reconfiguration.

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Section: Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%