Neural Technologies for Precise Timing in Electric Power Systems with a Single-Frequency GPS Receiver

The global positioning system (GPS), with its ability to provide time synchronization with accuracy to 200 ns over the wide area, provides an ideal tool for performing time tagging in electric power systems. The observed GPS pseudo-range varies from the true range because of range measurement errors. GPS errors sources are ionospheric delays, atmospheric delays, troposphericelays, multi-path effects and dilution of precision etc., affecting the GPS signals as they travel from satellite to user on Earth. In this paper neural models has been presented are for more accurate GPS timing in electric power systems with a single-frequency GPS receiver. The proposed methods use back-propagation (BP), extended Kalman filter (EKF) and particle swarm optimization (PSO) training algorithms, which achieves the optimal training criterion. We use actual data to evaluate the performance of the proposed methods. An experimental test setup is designed and implemented for this purpose. Results using the three methods are discussed. The experimental results obtained from a coarse acquisition (C/A)-code single-frequency GPS receiver are provided to confirm the efficacy of the approaches to give high accurate timing. The GPS timing RMS error using neural network based on PSO learning algorithm reduces to less than 105 and 36 ns, before and after SA, respectively.