Fault Detection and Mitigation for Revenue Loss Minimization

Transmission line fault detection an aspect of monitoring power system, its detect faults condition able to equipment failure if adequate action not taken. This paper considered performance as key criterion for analysis. Failures may result into downtime, equipment damage, high damper to machine as negative impact on network operations. Simulation results shows faults detection of Trans Amadi Industrial layout. The techniques proposed three-phase current and voltage as input study. Examination of three stages, feed forward neural network with back propagation algorithm for detection and classification by artificial neural network capacity to detect faults. ANN design anticipate occurrence of system abnormalities, during training and identification. Techniques engaged simulation of various parameters cases on the view to determine stability. The proposed paper is used to identify, classify reliable system states in order to provide electric utilities with adequate information for response.