Recurrent polynomial neural networks for enhancing performance of GPS based line fault location

A power line fault produces a fast rise-time traveling wave that emanates from the fault point and propagates throughout the power grid. Each remote time-tags the traveling wave leading edge as it passes through each corresponding substation equipped with a fault locator remote. The system requires a valid remote time tag on both sides of the fault point to calculate a faultpsilas location. Global Positioning System (GPS) is a worldwide satellite system that provides navigation, positioning, and timing for both military and civilian applications. This paper presents novel recurrent neural networks called the recurrent pi-sigma neural network (RPSNN) and recurrent sigma-pi neural network (RSPNN). The proposed NNs have been used as predictor in GPS receivers timing errors. The NNs were trained using the dynamic back propagation (BP) algorithm. The actual data collection was used to test the performance of the proposed NNs. The experimental results obtained from a coarse acquisition (C/A)-code single-frequency GPS receiver strongly support the potential of the method using RPSNN to give high accurate timing. The GPS timing RMS error reduces from 200 to less than 40 nanoseconds.