document: An on-line distributed intelligent fault section estimation system for large-scale power networks In this paper , a novel distributed intelligent system is suggested for on-line fault section estimation -LRB- FSE -RRB- of large-scale power networks . As the first step , a multi-way graph partitioning method based on weighted minimum degree reordering is proposed for effectively partitioning the original large-scale power network into the desired number of connected sub-networks with quasi-balanced FSE burdens and minimum frontier elements . After partitioning , a distributed intelligent system based on radial basis function neural network -LRB- RBF NN -RRB- and companion fuzzy system is suggested for FSE . The relevant theoretical analysis and procedure are presented in the paper . The proposed distributed intelligent FSE method has been implemented with sparse storage technique and tested on the IEEE 14 , 30 and 118-bus systems , respectively . Computer simulation results show that the proposed FSE method works successfully for large-scale power networks
doc_bio_tags: O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O B I I O O B I I I I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O
extractive_keyphrases: weighted minimum degree reordering minimum frontier elements distributed intelligent system radial basis function neural network fuzzy system sparse storage technique computer simulation
abstractive_keyphrases: on-line distributed intelligent fault section estimation system large-scale power networks on-line fault section estimation multi-way graph partitioning method based connected sub-networks quasi-balanced fse burdens ieee 14-bus systems ieee 30-bus systems ieee 118-bus systems