Advertisement

Simulation and real time analysis of network protection tripping strategy based on behavior trees

  • Xiong Haijun
  • Zhang Qi
Article
  • 59 Downloads

Abstract

The collaboration of multi intelligent electronic devices (IEDs) in a protection system can improve the both selectivity and real-time performance of the system. To verify the feasibility and real-time performance of a network trip strategy based on multi protection IEDs, a modeling and real time analysis method of the protection system based on behavior trees is proposed. The model of protection IED and circuit breaker in the network trip strategy is presented; the model of the delay behavior of the wide area network with normal topology is further more presented; using a network tripping strategy as example, the theory completion time of every behavior in the system has been calculated. The validity of our method was verified via three examples; this method can also complete the real-time analysis of isolated faulty components in a protection system under circumstances of line fault, circuit breaker failure, and protection component refuse movement.

Keywords

Intelligent electronic devices Network protection tripping Backup protection Behavior trees Real time 

Notes

Acknowledgements

This project supported by National Natural Science Foundation of China (51677072) and Chinese Universities Scientific Fund (2017MS154).

References

  1. 1.
    Farhangi, H.: The path of the smart grid [J]. IEEE Power Energy Mag. 8(1), 18–28 (2010)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Ross, K.J., Hopkinson, K.M., Pachter, M.: Using a distributed agent-based communication enabled special protection system to enhance smart grid security [J]. IEEE Trans. Smart Grid 4(2), 1216–1224 (2013)CrossRefGoogle Scholar
  3. 3.
    Fang, X., Misra, S., Xue, G., et al.: Smart grid- The new and improved power grid: a survey [J]. IEEE Commun. Surv. Tutor. 14(4), 944–980 (2012)CrossRefGoogle Scholar
  4. 4.
    Pipattanasomporn, M., Feroze, H., Rahman, S.: Multi-agent systems in a distributed smart grid: design and implementation[A]. In: Proceedings of the Power Systems Conference and Exposition, IEEE, pp. 1–8 (2009)Google Scholar
  5. 5.
    Karnouskos, S., De Holanda, T. N.: Simulation of a smart grid city with software agents[A]. In: Proceedings of the Third UKSim European Symposium on Computer Modeling and Simulation, IEEE, pp. 424–429 (2009)Google Scholar
  6. 6.
    YIN, Xiang-gen, WANG, Yang, ZHANG, Zhe: Zone-division and tripping strategy for limited wide area protection adapting to smart grid [J]. Proceed. CSEE 30(7), 1–7 (2010)Google Scholar
  7. 7.
    Zhu, Y., Song, S., Wang, D.: Multiagents-based wide area protection with best-effort adaptive strategy [J]. Int. J. Elec. Power Energy Syst. 31(2), 94–99 (2009)CrossRefGoogle Scholar
  8. 8.
    ZHANG, Jie, LU, De-hong: Analysis on application of automata theory in IEC 61850 [J]. Power Syst. Technol. 28(18), 34–38 (2004)Google Scholar
  9. 9.
    Lotfifard, S., Kezunovic, M., Mousavi, M.J.: Voltage sag data utilization for distribution fault location [J]. IEEE Trans. Power Deliv. 26(2), 1239–1246 (2011)CrossRefGoogle Scholar
  10. 10.
    Chen, J., Patton, R.J.: Robust Model-based Fault Diagnosis for Dynamic Systems [M]. Springer, Berlin (2012)MATHGoogle Scholar
  11. 11.
    Khorramdel, B., Marzooghi, H., Samet, H., et al.: Fault locating in large distribution systems by empirical mode decomposition and core vector regression[J]. Int. J. Elect. Power. Energy Syst. 58(6), 215–225 (2014)CrossRefGoogle Scholar
  12. 12.
    Huang, F., Wang, X., Dong, X., Xueyuan, W.: A simulation for smart distribution grid communication system[J]. Autom. Elec. Power Syst. 37(11), 81–86 (2013)Google Scholar
  13. 13.
    Ding, L., Wang, X., Tong, X.: IEC 61850 sampled value transmission simulation based on EPOCHS [J]. Autom. Elec. Power Syst. 32(20), 67–72 (2008)Google Scholar
  14. 14.
    Chen, G., Zhang, Z., Yin, X., Wang, F.: Wide area backup protection communication mode and its performance evaluation [J]. Proc. CSEE 34(1), 186–196 (2014)Google Scholar
  15. 15.
    Tong, X., Wang, X.R., Hopkinson, K., Tang, J.: Simulation modeling and implements of wide-area backup protection multi-agent system [J]. Proc. CSEE 28(19), 111–117 (2008)Google Scholar
  16. 16.
    Dromey, R. G.: From requirements to design: Formalizing the key steps [A]. In: Proceedings of the First International Conference on Software Engineering and Formal Methods, IEEE, pp. 2–11 (2003)Google Scholar
  17. 17.
    Milosevic, Z., Dromey, R. G.: On expressing and monitoring behaviour in contracts [A]. In: Proceedings of the Enterprise Distributed Object Computing Conference, EDOC’02[C], IEEE, pp. 3–14 (2002)Google Scholar
  18. 18.
    Smith, C., Winter, K., Hayes, I., et al. An environment for building a system out of its requirements[A]. In: Proceedings of the 19th IEEE international conference on Automated software engineering[C], IEEE Computer Society, pp. 398–399 (2004)Google Scholar
  19. 19.
    Dromey, R.G.: Genetic Design: Amplifying Our Ability to Deal with Requirements Complexity [M]. Springer, Berlin (2005)Google Scholar
  20. 20.
    Wen, L., Dromey, R. G.: From requirements change to design change: A formal path[A]. In: Proceedings of the Second International Conference on Software Engineering and Formal Methods, IEEE, pp. 104–113 (2004)Google Scholar
  21. 21.
    Winter, K., Hayes, I. J., Colvin, R.: Integrating requirements: the Behavior Tree philosophy [A]. In: Proceedings of the IEEE International Conference on Software Engineering and Formal Methods[C], IEEE, pp. 41–50 (2010)Google Scholar
  22. 22.
    Winter, K., Colvin, R., Dromey, R. G.: Dynamic Relational Behaviour for Large-Scale Systems [A]. In: Proceedings of the Software Engineering Conference, 2009[C], Australian, IEEE, pp. 173–182 (2009)Google Scholar
  23. 23.
    Lim, C.U., Baumgarten, R., Colton, S.: Evolving Behaviour Trees for the Commercial Game DEFCON[M]. Springer, Berlin (2010)CrossRefGoogle Scholar
  24. 24.
    Perez, D., Nicolau, M., O’Neill, M., et al.: Evolving Behaviour Trees for the Mario ai Competition Using Grammatical Evolution [M]. Springer, Berlin (2011)CrossRefGoogle Scholar
  25. 25.
    Yatapanage, N., Winter, K., Zafar, S.: Slicing Behavior Tree Models for Verification [M]. Springer, Berlin (2010)Google Scholar
  26. 26.
    Lindsay, P.A., Yatapanage, N., Winter, K.: Cut set analysis using behavior trees and model checking[J]. For. Aspects Comput. 24(2), 249–266 (2012)CrossRefGoogle Scholar
  27. 27.
    Haijun, X.I., Yongli, Z.H., Zhang, F.: Formal specification and verification of IEC61850 IED interoperabilitybased on behaviourtree[J]. Autom. Elect. Power Syst. 37(24), 66–71 (2013)Google Scholar
  28. 28.
    Xiong, H.-J., Wang, X.-H., Zhu, Y., Zhang, C.-M.: IEC61850 A specification and verification method of real-time interoperability for IEC61850 IED [J]. Autom. Elect. Power Syst. 38(19), 90–95 (2014)Google Scholar
  29. 29.
    Chen, G.Y., Yin, X., Zhang, K.: Communication modeling for wide-area relay protection based on IEC61850[J]. TELKOMNIKA Indones. J. Elect. Eng. 10(7), 1673–1684 (2012)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Control and Computer EngineeringNorth China Electric Power UniversityBaodingChina
  2. 2.School of Science and TechnologyNorth China Electric Power UniversityBaodingChina

Personalised recommendations