Distributed fault section estimation system using radial basis function neural network and its companion fuzzy system
The distributed radial basis function neural networks (RBF NN) can effectively solve fault section estimation (FSE) in large-scale power networks. However, when network expands or topology changes, the RBF NN has to be totally retrained, which is time-consuming and becomes a bottleneck in its applications. In this paper, functional equivalence between a RBF NN and a companion fuzzy system (CFS) is built up throughout the neural network training process, therefore the black-box-like knowledge in a RBF NN will be rule-based and transparent in its CFS. Through useful knowledge extraction from the old CFS and insertion back to the new CFS piece by piece, the RBF NN retraining issue under network expansion and topology change can be solved effectively and efficiently. In the paper, the conditions for equivalence of a RBF NN and its CFS are illustrated. The novel procedure for RBF NN retraining under network expansion and topology change is presented. The corresponding FSE system has been implemented and tested in the IEEE 118-bus power system. The simulation results show that the suggested approach for RBF NN retraining works successfully and efficiently in the case of power network expansion and topology change, which significantly improves the application potential of RBF NN in FSE of practical power systems.
fault section estimation companion fuzzy system distributed radial basis function neural networks large-scale power networks