Advertisement

Transmission lines’ fault detection using syntactic pattern recognition

  • Christos Pavlatos
  • Vasiliki Vita
  • Alexandros C. Dimopoulos
  • Lambros Ekonomou
Original Paper
  • 31 Downloads

Abstract

In this paper, an efficient hardware relay is presented that is implemented based on syntactic pattern recognition techniques. The proposed system is capable of detecting faults existing at power system waveforms in \(\upmu \)s, after reading each peak of the examined signal, aiming to reduce or even to totally prevent safety problems and economic losses. In order syntactic pattern recognition methods to be utilized as a recognition tool of waveforms at transmission lines, the tasks of selecting appropriate primitive patterns, determining the linguistic representation and forming a suitable grammar, should be developed. In this study, attribute grammars have been selected to model the examined signals due to their power to describe syntactic and semantic knowledge. The hardware implementation of the suggested relay, that stands on Earley’s parsing algorithm, is developed using Verilog hardware description language, downloaded on a Virtex 7 XILINX FPGA board and evaluated through real waveforms and data received from IPTO. The obtained results have shown that the presented system could be an efficient alternative tool in the field of transmission lines’ fault detection.

Keywords

Fault detection Transmission lines Power system waveforms FPGA and syntactic pattern recognition 

References

  1. 1.
    Independent power transmission operator (IPTO or ADMIE). http://www.admie.gr/nc/en/home/. Accessed 28 Dec 2017
  2. 2.
  3. 3.
    Aho, A.V., Lam, M.S., Sethi, R., Ullman, J.D.: Compilers: Principles, Techniques, and Tools, 2nd edn. Addison Wesley, Boston (2006)zbMATHGoogle Scholar
  4. 4.
    Chiang, Y., Fu, K.: Parallel parsing algorithms and VLSI implementations for syntactic pattern recognition. IEEE Trans. Pattern Anal. Mach. Intell. 6, 302–314 (1984)CrossRefzbMATHGoogle Scholar
  5. 5.
    Chomsky, N.: Three models for the description of language. IRE Trans. Inf. Theory 2(3), 113–124 (1956).  https://doi.org/10.1109/TIT.1956.1056813 CrossRefzbMATHGoogle Scholar
  6. 6.
    Das, B., Reddy, J.V.: Fuzzy-logic-based fault classification scheme for digital distance protection. IEEE Trans. Power Deliv. 20(2), 609–616 (2005)CrossRefGoogle Scholar
  7. 7.
    Dimopoulos, A., Pavlatos, C., Papakonstantinou, G.: A platform for the automatic generation of attribute evaluation hardware systems. Comput. Lang. Syst. Struct. 36(2), 203–222 (2010).  https://doi.org/10.1016/j.cl.2009.09.003 Google Scholar
  8. 8.
    Earley, J.: An efficient context-free parsing algorithm. Commun. ACM 13(2), 94–102 (1970).  https://doi.org/10.1145/362007.362035 CrossRefzbMATHGoogle Scholar
  9. 9.
    Geng, T., Xu, F., Mei, H., Meng, W., Chen, Z., Lai, C.: A practical GLR parser generator for software reverse engineering. J. Netw. 9(3), 769–776 (2014)Google Scholar
  10. 10.
    Graham, S.L., Harrison, M.A., Ruzzo, W.L.: An improved context-free recognizer. ACM Trans. Program. Lang. Syst. 2(3), 415–462 (1980)CrossRefzbMATHGoogle Scholar
  11. 11.
    Gupta, S., Kazi, F., Wagh, S., Kambli, R.: Neural network based early warning system for an emerging blackout in smart grid power networks. In: Intelligent Distributed Computing, pp. 173–183. Springer, New York (2015)Google Scholar
  12. 12.
    Hopcroft, J.E., Ullman, J.D.: Formal Languages and Their Relation to Automata. Addison-Wesley Longman Publishing Co. Inc, Boston (1969)zbMATHGoogle Scholar
  13. 13.
    Hopcroft, J.E., Ullman, J.D.: Formal languages and their relation to automata. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA (1969)zbMATHGoogle Scholar
  14. 14.
    Jamehbozorg, A., Shahrtash, S.: A decision tree-based method for fault classification in double-circuit transmission lines. IEEE Trans. Power Deliv. 25(4), 2184–2189 (2010)CrossRefGoogle Scholar
  15. 15.
    Kamel, T.S., Hassan, M.A., Morshedy, A.E.: Advanced distance protection scheme for long transmission lines in electric power systems using multiple classified ANFIS networks. In: 5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, pp. 1–5 (2009).  https://doi.org/10.1109/ICSCCW.2009.5379442
  16. 16.
    Kim, C.H., Aggarwal, R.: Wavelet transforms in power systems. Part 1: General introduction to the wavelet transforms. Power Eng. J. 14(2), 81–87 (2000)CrossRefGoogle Scholar
  17. 17.
    Ngaopitakkul, A., Apisit, C., Bunjongjit, S., Pothisarn, C.: Identifying types of simultaneous fault in transmission line using discrete wavelet transform and fuzzy logic algorithm. Int. J. Innov. Comput. Inf. Control 9(7), 2701–2712 (2013)Google Scholar
  18. 18.
    Parikh, U.B., Das, B., Maheshwari, R.: Fault classification technique for series compensated transmission line using support vector machine. Int. J. Electr. Power Energy Syst. 32(6), 629–636 (2010)CrossRefGoogle Scholar
  19. 19.
    Pavlatos, C., Dimopoulos, A., Papakonstantinou, G.: Parallel hardware stochastic context-free parsers. Int. J. Pattern Recognit. Artif. Intell. 30(4) (2016).  https://doi.org/10.1142/S0218001416500087
  20. 20.
    Pavlatos, C., Dimopoulos, A.C., Koulouris, A., Andronikos, T., Panagopoulos, I., Papakonstantinou, G.: Efficient reconfigurable embedded parsers. Comput. Lang. Syst. Struct. 35(2), 196–215 (2009).  https://doi.org/10.1016/j.cl.2007.08.001 Google Scholar
  21. 21.
    Pavlatos, C., Panagopoulos, I., Papakonstantinou, G.: A programmable pipelined coprocessor for parsing applications. In: Workshop on Application Specific Processors (WASP) CODES, Stockholm (2004)Google Scholar
  22. 22.
    Pavlatos, C., Vita, V.: Linguistic representation of power system signals. In: Electricity Distribution, pp. 285–295 (2016)Google Scholar
  23. 23.
    Prasad, C., Prasad, D.: Fault detection and phase selection using Euclidean distance based function for transmission line protection. In: International Conference on Advances in Electrical Engineering (ICAEE), Vellore (2014)Google Scholar
  24. 24.
    Qi, X., Wen, M., Yin, X., Zhang, Z., Tang, J., Cai, F.: A novel fast distance relay for series compensated transmission lines. Int. J. Electr. Power Energy Syst. 64, 1–8 (2015).  https://doi.org/10.1016/j.ijepes.2014.07.028 CrossRefGoogle Scholar
  25. 25.
    Raikwar, V.T., Kankale, R.S., Jadhav, S.S.: Ehv transmission line fault classification. Int. J. Manag. Appl. Sci. (IJMAS) 2(7), 68–72 (2014)Google Scholar
  26. 26.
    Ruzzo, W.L.: General context-free language recognition. Ph.D. thesis. University of California, Berkeley (1978)Google Scholar
  27. 27.
    Saha, M.M., Izykowski, J.J., Rosolowski, E.: Fault Location on Power Networks. Springer, New York (2009)Google Scholar
  28. 28.
    Samantaray, S.: A systematic fuzzy rule based approach for fault classification in transmission lines. Appl. Soft Comput. 13(2), 928–938 (2013)CrossRefGoogle Scholar
  29. 29.
    Sipser, M.: Introduction to the Theory of Computation, vol. 2. Thomson Course Technology, Boston (2006)zbMATHGoogle Scholar
  30. 30.
    Tomita, M., Ng, S.K.: The generalized LR parsing algorithm. In: Tomita, M. (ed.) Generalized LR Parsing, pp. 1–16. Springer, New York (1991)CrossRefGoogle Scholar
  31. 31.
    Trahanias, P., Skordalakis, E.: Syntactic pattern recognition of the ECG. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 648–657 (1990).  https://doi.org/10.1109/34.56207 CrossRefGoogle Scholar
  32. 32.
    Upendar, J., Gupta, C., Singh, G.: Discrete wavelet transform and genetic algorithm based fault classification of transmission systems. In: International Journal of Innovative Computing, Information and Control, pp. 323–328 (2008)Google Scholar
  33. 33.
    Valsan, S.P., Swarup, K.: Fault detection and classification logic for transmission lines using multi-resolution wavelet analysis. Electr. Power Compon. Syst. 36(4), 321–344 (2008)CrossRefGoogle Scholar
  34. 34.
    Valsan, S.P., Swarup, K.S.: High-speed fault classification in power lines: theory and FPGA-based implementation. IEEE Trans. Ind. Electron. 56(5), 1793–1800 (2009).  https://doi.org/10.1109/TIE.2008.2011055 CrossRefGoogle Scholar
  35. 35.
    Xu, J., Meyer-Baese, U., Huang, K.: FPGA-based solution for real-time tracking of time-varying harmonics and power disturbances. Int. J. Power Electron. 4(2), 134–159 (2012)CrossRefGoogle Scholar
  36. 36.
    Yadav, A., Swetapadma, A.: A novel transmission line relaying scheme for fault detection and classification using wavelet transform and linear discriminant analysis. Ain Shams Eng. J. 6(1), 199–209 (2015)CrossRefGoogle Scholar
  37. 37.
    Younger, D.H.: Recognition and parsing of context-free languages in \(n^3\). Inf. Control 10, 189–208 (1967)CrossRefzbMATHGoogle Scholar
  38. 38.
    Zarbita, S., Lachouri, A., Boukadoum, H.: A new approach of fast fault detection in hv-b transmission lines based on detail spectrum energy analysis using oscillographic data. Int. J. Electr. Power Energy Syst. 73, 568–575 (2015).  https://doi.org/10.1016/j.ijepes.2015.05.047 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Christos Pavlatos
    • 1
  • Vasiliki Vita
    • 2
  • Alexandros C. Dimopoulos
    • 3
  • Lambros Ekonomou
    • 2
  1. 1.Department of Computer ScienceHellenic Air Force AcademyAcharnesGreece
  2. 2.Department of Electrical and Electronic Engineering EducatorsA.S.PE.T.E., School of Pedagogical and Technological EducationHeraklionGreece
  3. 3.Department of Informatics and TelematicsHarokopio University of AthensTavrosGreece

Personalised recommendations