摘要
Global Positioning System (GPS) is a worldwide satellite system that provides navigation, positioning, and timing for both military and civilian applications. GPS based time reference provides inexpensive but highly-accurate timing and synchronization capability and meets requirements in power system fault location, monitoring, and control. In the present era of restructuring and modernization of electric power utilities, the applications of GIS/GPS technology in power industry are growing and covering several technical and man-agement activities. Because of GPS receiver’s error sources are time variant, it is necessary to remove the GPS measurement noise. 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 ex-perimental 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.
Global Positioning System (GPS) is a worldwide satellite system that provides navigation, positioning, and timing for both military and civilian applications. GPS based time reference provides inexpensive but highly-accurate timing and synchronization capability and meets requirements in power system fault location, monitoring, and control. In the present era of restructuring and modernization of electric power utilities, the applications of GIS/GPS technology in power industry are growing and covering several technical and man-agement activities. Because of GPS receiver’s error sources are time variant, it is necessary to remove the GPS measurement noise. 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 ex-perimental 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.
