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Error Reduction for GPS Accurate Timing in Power Systems using Kalman Filters and Neural Networks

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
Metody zmniejszenia błędów systemu GPS przy wykorzystaniu filtrów Kalmana i sieci neuronowych
Języki publikacji
EN
Abstrakty
EN
The Global Positioning System (GPS) based time reference provides inexpensive but highly-accurate timing and synchronization capability and meets requirements in power system fault location, monitoring, and control. Precision satellite clocks and time measurements are the keys to the accuracy of GPS. A stand-alone civilian user enjoys an accuracy of 25 meters and 200 nanoseconds. Five methods, including two methods using Kalman Filter (KF), Recurrent Neural Network (RNN), Pi-Sigma Neural Network (PSNN) and Sigma-Pi Neural Network (SPNN), are proposed for error reduction of GPS receivers timing data. 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 five methods are discussed. The experimental results obtained from a Coarse Acquisition (C/A)-code single-frequency GPS receiver strongly support the potential of the method using PSNN to give high accurate timing. The GPS timing RMS error reduces to less than 38 nanoseconds.
PL
Opisano zastosowanie systemu pozycjonowania GPS lokalizacji uszkodzeń i monitorowania sieci przesyłowej. System cywilny GPS oferuje dokładność rzędu 25 m i 200 nanosekund. Opracowano szereg metod poprawy dokładności, wykorzystujących filtry Kalmana i sieci neuronowe. Zredukowano błąd taktowania do około czterdziestu nanosekund.
Rocznik
Strony
161--168
Opis fizyczny
Bibliogr. 16 poz., il., tabl., wykr.
Twórcy
  • Electronic Engineering from Department of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran, M_Mosavi@iust.ac.ir
Bibliografia
  • [1] B. W. Parkinson, J. J. Spilker Jr, P. Axelrad and P. Enge, Global Positioning System: Theory and Applications, The American Institute of Aeronautics and Astronautics, 1996.
  • [2] K. L. V. Dyke, The World after SA: Benefits to GPS Integrity, IEEE Conference on Position, Location and Navigation, pp.387-394, 2000.
  • [3] M. R. Mosavi, Neural Networks-based Single-Frequency GPS Receivers Ionospheric Time-Delay Modeling, Asian Journal of Geoinformatics, Vol.9, No.4, pp.35-40, 2009.
  • [4] O. Øvstedal, Absolute Positioning with Single-Frequency GPS Receivers, Journal of GPS Solutions, Vol.5, No.4, pp.33-44, 2002.
  • [5] M. R. Mosavi, Estimation of Pseudo-Range DGPS Corrections using Neural Networks Trained by Evolutionary Algorithms, Journal of Review of Electrical Engineering, Vol.5, No.6, 2010.
  • [6] 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.
  • [7] M. R. Mosavi, A Comparative Study between Performance of Recurrent Neural Network and Kalman Filter for DGPS Corrections Prediction, IEEE Conference on Signal Processing (ICSP 2004), Vol.1, pp.356-359, 2004.
  • [8] M. S. Kim and S. G. Kong, Parallel-Structure Fuzzy Systems for Time Series Prediction, Journal of Fuzzy Systems, Vol.3, No.1, pp.331-340, 2001.
  • [9] A. G. Phadke, Synchronized Phasor Measurements in Power Systems, IEEE Computer Applications in Power, pp.11-15, 1993.
  • [10] M. R. Mosavi, Comparing DGPS Corrections Prediction using Neural Network, Fuzzy Neural Network and Kalman Filter, Journal of GPS Solutions, Vol.10, No.2, pp.97-107, 2006.
  • [11] F. D. Marques, L. F. R Souza, D. C. Rebolho, A. S. Caporali, E. M. Belo and R. L. Ortolan, Application of Time-Delay Neural and Recurrent Neural Networks for the Identification of a Hingeless Helicopter Blade Flapping and Torsion Motions, Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol.27, No.2, pp.97-103, 2005.
  • [12] J. Sang, K. Kubik and L. Zhang, Prediction of DGPS Corrections with Neural Networks, IEEE Conference on Knowledge-based Intelligent Electronic Systems, pp.355-361, 1997.
  • [13] M. R. Mosavi, A Practical Approach for Accurate Positioning with L1 GPS Receivers using Neural Networks, Journal of Intelligent and Fuzzy Systems, Vol.17, No.2, pp.159-171, 2006.
  • [14] MicroTracker LP Designer’s Guide, Rockwell International Corporation, GPS-22, 1995.
  • [15] A. Indriyatmoko, T. Kang, Y. J. Lee, G. I. Jee, Y. B. Cho and J. Kim, Artificial Neural Networks for Predicting DGPS Carrier Phase and Pseudo-Range Correction, Journal of GPS Solutions, Vol.12, No.4, pp.237-247, 2008.
  • [16] I. Sadinezhad and M. Joorabian, An Adaptive Precise One End Fault Location in Transmission Lines Based on Hybrid Complex Least Error Squares Algorithm and Adaptive Artificial Neural Networks, Journal of Review of Electrical Engineering, Vol.3, No.5, pp.803-810, 2008.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA7-0056-0033
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