Abstract
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.
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Wang, L., Fernandez, J., Burgett, J., Conners, R.W., & Liu, Y. (2008). An evaluation of network time protocol for clock synchronization in wide area measurements. IEEE conference on conversion and delivery of electrical energy in the 21st century (pp. 1–5).
Parkinson, B. W., Spilker, J. J, Jr., Axelrad, P., & Enge, P. (1996). Global positioning system: Theory and applications. Washington, DC: The American Institute of Aeronautics and Astronautics.
Øvstedal, O. (2002). Absolute positioning with single-frequency GPS receivers. Journal of GPS Solutions, 5(4), 33–44.
Mosavi, M. R. (2009). Neural networks-based single-frequency GPS receivers ionospheric time-delay modeling. Asian Journal of Geoinformatics, 9(4), 35–40.
Achanta, R. (2004). Detection and correction of global positional system carrier phase measurement anomalies. M.S. Thesis, Ohio University.
Kaplan, E. D. (1996). Understanding GPS: Principles and applications. USA: Artech House.
Braasch, M. S. (2001). Performance comparison of multipath mitigating receiver architecture. In IEEE conference on aerospace (pp. 1309–1315).
Dyke, K. L. V. (2000). The world after SA: Benefits to GPS integrity. Location and navigation: IEEE conference on position (pp. 387–394).
Mosavi, M. R. (2006). Comparing DGPS corrections prediction using neural network, fuzzy neural network and Kalman filter. Journal of GPS Solutions, 10(2), 97–107.
Mosavi, M. R. (2006). A practical approach for accurate positioning with L1 GPS receivers using neural networks. Journal of Intelligent and Fuzzy Systems, 17(2), 159–171.
Phadke, A. G. (1993). Synchronized phasor measurements in power systems. In IEEE Magazine on computer applications in power (pp. 11–15).
Carta, A., Locci, N., Muscas, C., & Sulis, S. (2008). A flexible GPS-based system for synchronized phasor measurement in electric distribution networks. IEEE Transactions on Instrumentation and Measurement, 57(11), 2450–2456.
Street, M. A., Thurein, I. P., & Martin, K. E. (1995). Global positioning system applications at the Bonneville power administration. In IEEE technical applications conference (pp. 244–251).
Jafarian, P., & Sanaye-Pasand, M. (2010). A traveling-wave-based protection technique using wavelet/PCA analysis. IEEE Transactions on Power Delivery, 25(2), 588–599.
Kim, M. S., & Kong, S. G. (2001). Parallel-structure fuzzy systems for time series prediction. Journal of Fuzzy Systems, 3(1), 331–340.
Mosavi, M. R. (2007). Precise real-time positioning with a low cost GPS engine using neural networks. Journal of Survey Review, 39(306), 316–327.
Shuhui, L. (2001). Comparative analysis of backpropagation and extended Kalman filter in pattern and batch forms for training neural networks. In IEEE conference on neural networks (pp. 144–149).
Ibrahim, F., & Tascillo, A. (2000). DGPS/INS integration using neural network methodology. In IEEE conference on tools with artificial intelligence (pp. 114–121).
Huang, R. C., & Chen, M. S. (2000). Adaptive equalization using complex-valued multilayered neural network based on the extended Kalman filter. IEEE Conference on Signal Processing, 1, 519–524.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. IEEE conference on neural networks (pp. 1942–1948).
Chun-tao, M., Kun, W., & Li-yong, Z. (2009). A new training algorithm for RBF neural network based on PSO and simulation study. In IEEE congress on computer science and information, engineering (pp. 641–645).
Zhao, F., Ren, Z., Yu, D., & Yang, Y. (2005). Application of an improved particle swarm optimization algorithm for neural network training. IEEE Conference on Neural Networks and Brain, 3, 1693–1698.
Sellles, M., & Rylander, B. (2002). Neural network learning using particle swarm optimizers. Advances in Information Science and Soft Computing, 22(11), 224–226.
Rockwell International Corporation. (1995). MicroTracker LP Designer’s Guide, GPS-22.
Vizireanu, D. N. (2011). A simple and precise real-time four point single sinusoid signals instantaneous frequency estimation method for portable DSP based instrumentation measurement. Journal of Measurement, 44(2), 500–502.
Mosavi, M. R., & EmamGolipour, I. (2013). De-noising of GPS receivers positioning data using wavelet transform and bilateral filtering. Journal of Wireless Personal Communications, 71(3), 2295–2312.
Reis, J., Sanguino, J., & Rodrigues, A. (2012). Baseline influence on single-frequency GPS precise heading estimation. Journal of Wireless Personal Communications, 64, 185–196.
Mosavi, M. R., Soltani Azad, M., & EmamGholipour, I. (2013). Position estimation in single-frequency GPS receivers using Kalman filter with pseudo-range and carrier phase measurements. Journal of Wireless Personal Communications. doi:10.1007/s11277-013-1166-0.
Mohammadi, K., & Refan, M. H. (2002). A new method for improving of GPS receivers time accuracy using Kalman filter. Journal of Engineering Science, Iran University of Science and Technology, 13(1), 11–24.
Mosavi, M. R. (2007). GPS receivers timing data processing using neural networks: optimal estimation and errors modeling. Journal of Neural Systems, 17(5), 383–393.
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This research was supported by Iran University of Science and Technology Grants.
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Mosavi, M.R., Nabavi, H. & Nakhaei, A. Neural Technologies for Precise Timing in Electric Power Systems with a Single-Frequency GPS Receiver. Wireless Pers Commun 75, 925–941 (2014). https://doi.org/10.1007/s11277-013-1398-z
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DOI: https://doi.org/10.1007/s11277-013-1398-z