Efficient multiway graph partitioning method for fault section estimation in large-scale power networks

Fault section estimation (FSE) of large-scale power networks can be implemented effectively by the distributed artificial intelligence (AI) technique. In this paper, an efficient multiway graph partitioning method is proposed to partition the large-scale power networks into the desired number of connected subnetworks with balanced working burdens in performing FSE. The number of elements at the frontier of each subnetwork is also minimised in the method. The suggested method consists of three basic steps: forming the weighted depth-first-search tree of the studied power network; partitioning the network into connected, balanced subnetworks and minimising the number of the frontier nodes of the subnetworks through iterations so as to reduce the interaction of FSE in adjacent subnetworks. The relevant mathematical model and partitioning procedure are presented. The method has been implemented with the sparse storage technique and tested in the IEEE 14-bus, 30-bus and 118-bus systems, respectively. Computer simulation results show that the proposed multiway graph partitioning method is effective for the large-scale power system FSE using the distributed AI technique