Skip to main content

Advertisement

SpringerLink
Log in

Redundant placement of phasor measurement unit using multi objective evolutionary algorithms based on modified spectral clustering

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Optimal placement of phasor measurement unit (OPPMU) considering controlled islanding is a high dimensional constrained optimization problem that determines optimum location of PMU. Controlled islanding can be used as an effective emergency action to split a large scale power system into islands for avoiding blackouts. This paper proposes an optimal PMU placement model considering power system controlled islanding. So that the power network remains observable under controlled islanding as well as normal operating condition. A new method based on spectral clustering is proposed to handle multiple constraints in order to guarantee the stability of generated islands. The objective function used in this controlled islanding algorithm is the minimal power flow disruption. Constraints such as generator coherency, load–generation imbalance are considered for spectral clustering. OPPMU is formed for the obtained islands using two conflicting objectives of minimizing the number of PMU and maximizing the measurement redundancy using conventional mixed integer linear programming, real coded genetic algorithm(RGA), non dominated sorting differential evolutionary algorithm, and modified non dominated sorting GA II (MNSGA II). RGA with simulated binary crossover and non dominated sorting differential evolution with improved crowding distance and mutation is implemented. In MNSGA II proposed a variant which reduces the run-time complexity using the technique space–time-trade-off. Simulation results using NE 39 and IEEE 118 bus test systems show that the proposed method is computationally efficient when solving controlled islanding based OPPMU problem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Wilson, R.J.: Introduction to Graph Theory. Longman Group Ltd. ISBN 0-582-24993-7 (1996)

  2. Sun, K., Zheng, Z., Lu, Q.: Splitting strategies for islanding operation of large scale power systems using OBDD-based methods. IEEE Trans. Power Syst. 18(2), 912–923 (2003). https://doi.org/10.1109/TPWRS.2003.810995

  3. Yang, B., Vital, V., Heydt, G.T.: Slow coherency based controlled islanding—a demonstration of the approach on the August 14, 2003 Blackout Scenario. IEEE Trans. Power Syst. 21, 1840–1847 (2006). https://doi.org/10.1109/TPWRS.2006.881126

  4. Sun, K., Zheng, D.Z., Lu, Q.: A simulation study of OBDD-based proper splitting strategies for power systems under consideration of transient stability. IEEE Trans. Power Syst. 20(1), 389–399 (2005). https://doi.org/10.1109/TPWRS.2003.818747

  5. Xu, G., Vittal, V., Meklin, A., Thalman, J.E.: Controlled islanding demonstrations on the WECC system. IEEE Trans. Power Syst. (2011). https://doi.org/10.1109/TPWRS.2010.2047413

  6. Ding, T., Sun, K., Huang, C.: Mixed Integer linear programming based splitting strategies for power system islanding operation considering network connectivity. IEEE Syst. J. (2015). https://doi.org/10.1109/JSYST.2015.2493880

  7. Gomez, O., Rios, M.A.: Real time identification of coherent groups for controlled islanding based on graph theory. IET J. Gener. Transm. Distrib. 9(8), 748–758 (2015). https://doi.org/10.1049/iet-gtd.2014.0865

  8. Ding, L., Ma, Z., Wall, P.: Graph spectra based controlled islanding for low inertia power systems. IEEE Trans. Power Deliv. 32(1), 302–309 (2017). https://doi.org/10.1109/TPWRD.2016.2582519

  9. Fang, N., Zhou, J.: A crossover of real coded genetic and artificial fish swarm algorithm for short term optimal hydrothermal scheduling. Int. J. Electr. Power Energy Syst. 62, 617–629 (2014). https://doi.org/10.1016/j.jepes.2014.05.017

  10. Aghaie, M., Norouzi, A., Zolfaghari, A.: Advanced progressive real coded genetic algorithm for nuclear system availability optimization through preventive maintenance scheduling. Int. J. Ann. Nucl. Energy 60, 64–72 (2013). https://doi.org/10.1016/j.anucene.2013.04.023

  11. Juang, J.-G., Huang, M.-T., Liu, W.-K.: PID control using preresearched genetic algorithm for MIMO system. IEEE Trans. Syst. Man Cybern. C (2008). https://doi.org/10.1109/TSMCC.2008.923890

  12. Jensen, M.T.: Reducing the run time complexity of multi objective EAs. The NSGAII and other algorithms. IEEE Trans. Evol. Comput. 7, 502–512 (2003)

    Google Scholar 

  13. Huang, L., Sun, Y., Xu, J., Gao, W., Zhang, J., Wu, Z.: Optimal PMU placement considering controlled islanding of power systems. IEEE Trans. Power Syst. (2014). https://doi.org/10.1109/TPWRS.2013.2285578

  14. Ahmed, S.S., Sarker, N.C., Khairuddin, A.B., Ghani, M.R.B.A., Ahmad, H.: A scheme for controlled islanding to prevent subsequent blackout. IEEE Trans. Power Syst. 18(1), 136–143 (2003). https://doi.org/10.1109/TPWRS.2002.807043

  15. Chow, J.H.: Time-Scale Modeling of Dynamic Matrix with Applications to Power Systems. Springer, New York (1982). http://www.springer.com/in/book/9783540121060

  16. Milosevic, B., Begovic, M.: Nondominated sorting genetic algorithm for optimal phasor measurement unit placement. IEEE Trans. Power Syst. 18(1), 69–75 (2003). https://doi.org/10.1109/MPER.2002.4311912

  17. Velmurugan, T., Santhanam, T.: Computational complexity between K-means and K-medoids clustering algorithms for normal and uniform distortions of data. J. Comput. Sci. 6(3), 363–368 (2010). https://doi.org/10.3844/jcssp.2010.363.368

  18. Henner, V.E.: A matrix separation scheme for emergency control. Int. J. Electr. Power Energy Syst. 2(2), 109–114 (1980). https://doi.org/10.1016/0142-0615(80)90017-4

  19. Phadke, A.: Synchronized phasor measurements in power systems. IEEE Comput. Appl. Power (1993). https://doi.org/10.1109/67.207465

  20. Esmail, M., Gharani, K., Shayanfar, H.A.: Redundant observability PMU placement in the presence of flow measurements considering contingencies. IEEE Trans. Power Syst. (2013). https://doi.org/10.1109/TPWRS.2013.2257883

  21. Nafeena, R., Iruthayarajan, M.W.: Redundant location of PMU for complete observability and wide area monitoring of power system using real coded genetic algorithm. Asian J. Res. Soc. Sci. Humanit. 6, 1131–1145 (2016). https://doi.org/10.5958/2249-7315.2016.01258.2

  22. Ahmadi, A., Alinejad-Beromi, Y., Moradi, M.: Optimal PMU placement for power system observability using binary particle swarm optimization and considering measurement redundancy. Int. J. Expert Syst. Appl. 38, 7263–7269 (2011). https://doi.org/10.1016/j.eswa.2010.12.025

  23. Deb, K.: Multiobjective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001). http://as.wiley.com/WileyCDA/WileyTitle/productCd-47187339X.html

  24. Deb, K., Kumar, A.: Real coded genetic algorithm with simulated binary crossover: studies on multi modal and multi objective problems. Complex Syst. 9, 431–454 (1995). http://www.complex-systems.com

  25. Storn, R., Price, K.A.: A simple and heuristic for global optimization over continuous spaces. ACM J. Global Optim. Syst. 11, 341–359 (1997). https://doi.org/10.1023/A:1008202821328

  26. Iruthayarajan, M.W., Baskar, S.: Evolutionary algorithm based design of multivariable PID controller. Int. J. Expert Syst. Appl. 36, 9159–9167 (2009). https://doi.org/10.1016/j.eswa.2011.09.152

  27. Sun, H.J., Peng, C.H., Guo, J.F.: Non-dominated sorting differential evolution algorithm for multi-objective integrated generation bidding and scheduling. In: IEEE International Conference on Intelligent Computing and Intelligent Systems, vol. 4, 20–22 November 2009. https://doi.org/10.1109/ICICISYS.2009.5357823

  28. D’Souza, R.G.L., Chandra Sekaran, K., Kandaswamy, A.: Improved NSGA-II based on a novel ranking scheme. J. Comput. 2(2). ISSN 2151-9617 (2010). https://sites.google.com/site/journalofcomputing/

Download references

Author information

Authors and Affiliations

  1. National Engineering College, Kovilpatti, Tamil Nadu, 630561, India

    Nafeena Rahamathullah & Willjuice Iruthayarajan Mariasiluvairaj

Authors
  1. Nafeena Rahamathullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Willjuice Iruthayarajan Mariasiluvairaj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nafeena Rahamathullah.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rahamathullah, N., Mariasiluvairaj, W.I. Redundant placement of phasor measurement unit using multi objective evolutionary algorithms based on modified spectral clustering. Cluster Comput 22 (Suppl 5), 12713–12725 (2019). https://doi.org/10.1007/s10586-018-1746-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1746-6

Keywords

Search

Navigation