Neuro-Prony and Taguchi's Methodology-Based Adaptive Autoreclosure Scheme for Electric Transmission Systems

This paper presents a novel intelligent autoreclosure technique to discriminate temporary faults from permanent faults, and to accurately determine fault extinction time. A variety of fault simulations is carried out on a specified transmission line on the standard IEEE 9-bus electric power system by using MATLAB/SimPowerSytems. Prony analysis is employed to extract data features from each simulated fault. The fault identification prior to reclosing is accomplished by an artificial neural network trained by Levenberg Marquardt and resilient backpropagation algorithms, which are developed by using MATLAB. Some important parameters which strongly affect the entire training process are fine-tuned to their corresponding best values with the help of Taguchi's method. Test results show the robustness and efficacy of the proposed autoreclosure scheme.