Minimum degree reordering based graph partitioning method for distributed fault section estimation system in power networks

Abstract

In order to make fault section estimation (FSE) in large-scale power networks use distributed artificial intelligence approach, we have to develop an efficient way to partition the large-scale power network into desired number of connected sub-networks such that each sub-network should have quasi-balanced working burden in performing FSE. In this paper, an efficient minimum degree reordering based graph partitioning method is suggested for the partitioning task. The method consists of two basic steps: partitioning the power network into connected, quasi-balanced and frontier minimized sub-networks based on minimum degree reordering and minimizing the number of the frontier nodes of the sub-networks through iterations so as to reduce the interaction of FSE in adjacent sub-networks. The partitioning procedure and characteristic analysis is presented. The method has been implemented with sparse storage technique and tested in the IEEE 14-bus, 30-bus and 118-bus systems respectively. Computer simulation results show that the proposed multiple-way graph partitioning method is suitable for FSE in large-scale power networks and is compared favorably with other graph partitioning methods suggested in references.