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GRAPH PARTITIONING METHOD FOR DISTRIBUTED FAULT SECTION ESTIMATION SYSTEM IN POWER NETWORKS

Bi Tianshu', Jiao Lianwei2, Yan Zheng', Ni Yixinl, Yang Qixun', C. M. Shenl, Felix F. Wul(1. The University of Hong Kong. Hong Kong, China)(2. Tsinghua Universitys Beijing 100084, China)(3. North China Electric Power University, Beijing 100085. China)  
Fault section estimation (FSE) of large--scale power networks can be implemented effectively by distributedartificial intelligence (Al) techniques. In this paper. an efficient multi--way graph partitioning method is proposed to partitionthe large--scale power networks into a desired number of connected sub--networks with balanced working burdens inperforming FSE. The number of elements at the frontier of each sub--network is minimized in the method as well. Theproposed method consists of three basic steps: forming the weighted depth--first--search tree of the studied power network:partitioning the network into connected, balanced ones and minimizing the number of the frontier nodes of the sub-networksso as to reduce the iteration of FSE among adjacent sub--networks. The method has been implemented with sparse storagetechnique and tested in the IEEE 14--bus. 30--bus and 118-bus systems respectively. Computer simulation results show thatthe proposed multi--way graph partitioning method is very fast and effective for large--scale power system FSE using adistributed Al technique.
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