New Training Strategies for RBF Neural Networks to Determine Fault Location in a Distribution Network with DG Units

This paper presents a new Radial Basis Function Neural Network with Optimum Steepest Descent (RBFNN-SD) learning algorithm for identifying the exact faulty line section in the distribution network with high penetration level of Distributed Generation (DG) Units. In the proposed method, to determine the exact fault location, two RBFNN-OSD have been developed for various fault types. The first RBFNN-OSD is used for predicting the fault distance from the source and all DG units while the second RBFNN is used for identifying the exact faulty line. Several case studies have been simulated to verify the accuracy of the proposed method. Furthermore, the results of RBFNN-OSD and RBFNN with conventional steepest descent algorithm are also compared. The results show that the proposed RBFNN-OSD can accurately determine the location of faults in a test given distribution system with several DG units.