Assessment of ANN-based auto-reclosing scheme developed on single machine-infinite bus model with IEEE 14-bus system model data

This paper focuses on methods to discriminate a temporary fault from a permanent one, and accurately determine fault extinction time in an extra high voltage (EHV) transmission line in a bid to develop a self-adaptive automatic reclosing scheme. Consequently, improper reclosing of the line onto a fault is avoided. The fault identification prior to reclosing is based on optimized artificial neural network associated with three different training algorithms. In addition, Taguchi´s methodology is employed in optimizing parameters that significantly influence during and post-training performance of the neural network. A comparison of overall performance of the three algorithms, developed and coded in MATLAB^TM software environment, is also presented. To validate the work, the developed technique in a single machine infinite bus (SMIB) model has been tested by data obtained from benchmark IEEE 14-bus system model simulations. The results show the efficacy of the developed adaptive automatic reclosing method.