Recurrent Polynomial Neural Networks for Enhancing Performance of GPS in Electric Systems
- 1 Associate Professor, Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Iran
Abstract
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.
- K. D. McDonald, “The modernization of GPS: Plans, new capabilities, and the future relationship to Galileo,” Journal of Global Positioning System, Vol. 1, No. 1, pp. 1–17, 2002.
- W. Lewandowski, J. Azoubib, and W. J. Klepczynski, “GPS: Primary tool for time transfer,” Proceedings of the IEEE, Vol. 87, No. 1, pp. 163–172, January 1999.
- T. E. Parker and D. Matsakis, “Time and frequency dis-semination advances in GPS transfer techniques,” GPS World Magazine, pp. 32–38, November 2004.
- K. Mohammadi and M. H. Refan, “A new method for improving of GPS receivers time accuracy using Kalman filter,” Journal of Engineering Science, Iran University of Science and Technology, Vol. 13, No. 1, pp. 11–24, 2002.
- M. R. Mosavi, “GPS receivers timing data processing using neural networks: Optimal estimation and errors modeling,” Journal of Neural Systems, Vol. 17, No. 5, pp. 383–393, October 2007.
- A. J. Hussain and P. Liatsis, “A new recurrent polynomial neural network for predictive image coding,” IEEE Con-ference on Image Processing and its Applications, Vol. 1, No. 465, pp. 82–86, 1999.
- K. E. Martin, “Precise timing in electric power systems,” IEEE Conference on Frequency Control, pp. 15–22, 1993.
- A. G. Phadke, “Synchronized phasor measurements in power systems,” IEEE Computer Applications in Power, pp. 11–15, April 1993.
- M. A. Street, I. P. Thurein, and K. E. Martin, “Global positioning system applications for enhancing the per-formance of large power systems,” CIGRE, pp. 1–6, 1994.
- H. Lee and A. M. Mousa, “GPS traveling wave fault lo-cator systems: Investigation into the anomalous meas-urements related to lighting strikes,” IEEE Transactions on Power Delivery, Vol. 11, No. 3, pp. 1214–1223, 1996.
- M. R. Mosavi, “Modeling of GPS SPS timing error using multilayered neural network,” IEEE Conference on Sig-nal Processing, China, November 16–19, 2006.
- M. R. Mosavi, “Real time prediction of GPS receivers timing errors using parallel-structure neural networks,” Journal of Geoinformatics, Vol. 3, No. 3, pp. 53–61, Sep-tember 2007.
- M. R. Mosavi, “A practical approach for accurate posi-tioning with L1 GPS receivers using neural networks,” Journal of Intelligent and Fuzzy Systems, Vol. 17, No. 2, pp. 159–171, March 2006.
- M. R. Mosavi, “Precise real-time positioning with a low cost GPS engine using neural networks,” Journal of Survey Review, Vol. 39, No. 306, pp. 316–327, October 2007.