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Optimal PMU placement for pessimistic dynamic vulnerability assessment

Optimal PMU placement for pessimistic dynamic vulnerability assessment

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This study proposes a novel optimal phasor measurement unit (PMU) placement method for dynamic vulnerability assessment with minimum number of PMUs. PMU measurements should reflect the change of power system status as earlier as possible when a contingency occurs. Therefore, those buses which are sensitive to the change of power system status have to be equipped with PMUs to monitor the fragile areas of power systems. First, a large power system is partitioned into several coherent clusters. Next, a probabilistic vulnerability index defined as the objective function and a binary quadratic programming model are proposed for this purpose. Finally, the proposed method is tested on IEEE 9-, 39-, and 145-bus systems. Results show that this method results in a PMU configuration with minimum number of devices and high vulnerability index. Such a PMU placement is able to estimate the vulnerability of power systems.

References

    1. 1)
      • 1. Ree, J.D.L., Centeno, V., Thorp, J.S., et al: ‘Synchronized phasor measurement applications in power systems’, IEEE Trans. Smart Grid, 2010, 1, (1), pp. 2027.
    2. 2)
      • 2. U.S. Department of Energy, Office of Electricity Delivery: ‘Factors affecting PMU installation costs’, https://www.smartgrid.gov/document/factors_affecting_pmu_installation_costs, accessed 25 May 2017.
    3. 3)
      • 3. Phadke, A.G., Thorp, J.S.: ‘Synchronized phasor measurements and their applications’ (Springer, USA, 2008).
    4. 4)
      • 4. Aghaei, J., Baharvandi, A., Rabiee, A., et al: ‘Probabilistic PMU placement in electric power networks: an MILP-based multiobjective model’, IEEE Trans. Ind. Inf., 2015, 11, (2), pp. 332341.
    5. 5)
      • 5. Gomez, O., Rios, M.A., Anders, G.: ‘Reliability-based phasor measurement unit placement in power systems considering transmission line outages and channel limits’, IET Gener. Transm. Distrib., 2014, 8, (1), pp. 121130.
    6. 6)
      • 6. Dehghani, M., Shayanfard, B., Khayatian, A.R.: ‘PMU ranking based on singular value decomposition of dynamic stability matrix’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 22632270.
    7. 7)
      • 7. Kamwa, I., Pradhan, A.K., Joos, G., et al: ‘Fuzzy partitioning of a real power system for dynamic vulnerability assessment’, IEEE Trans. Power Syst., 2009, 24, (3), pp. 13561365.
    8. 8)
      • 8. Xu, G., Vittal, V.: ‘Slow coherency based cutset determination algorithm for large power systems’, IEEE Trans. Power Syst., 2010, 25, (2), pp. 877884.
    9. 9)
      • 9. Tortós, J.Q., Valverde, G., Ding, L., et al: ‘Optimal placement of phasor measurement units to improve parallel power system restoration’. IEEE Innovative Smart Grid Technolnogy, 2011, pp. 17.
    10. 10)
      • 10. Karimipour, H., Dinavahi, V.: ‘Parallel domain-decomposition-based distributed state estimation for large-scale power systems’, IEEE Trans. Ind. Inf., 2016, 52, (2), pp. 12651269.
    11. 11)
      • 11. Kamwa, I., Grondin, R.: ‘PMU configuration for system dynamic performance measurement in large multiarea power systems’, IEEE Trans. Power Syst., 2002, 17, (2), pp. 385394.
    12. 12)
      • 12. Kamwa, I., Pradhan, A.K., Joos, G.: ‘Automatic segmentation of large power systems into fuzzy coherent areas for dynamic vulnerability assessment’, IEEE Trans. Power Syst., 2007, 22, (4), pp. 19741985.
    13. 13)
      • 13. Cepeda, J.C., Rueda, J.L., Erlich, I., et al: ‘Probabilistic approach-based PMU placement for real-time power system vulnerability assessment’. IEEE Innovative Smart Grid Technology, October 2012, pp. 18.
    14. 14)
      • 14. Rios, M.A., Gómez, O.: ‘Identification of coherent groups and PMU placement for inter-area monitoring based on graph theory’. Proc. IEEE PES Conf. Innovative Smart Grid Technologies (ISGT Latin America), October 2011, pp. 17.
    15. 15)
      • 15. Li, X., Wu, J., Long, C., et al: ‘A novel decomposition of power systems for PMU placement’. Control Conf. (CCC) 2015 34th Chinese, July 2015, pp. 89758980.
    16. 16)
      • 16. Priyadharshini, M., Meenakumari, R.: ‘Probabilistic approach based optimal placement of phasor measurement units via the estimation of dynamic vulnerability assessment’. Int. Conf. Green Computing Communication and Electrical Engineering (ICGCCEE), March 2014, pp. 17.
    17. 17)
      • 17. Rashidi, M., Farjah, E.: ‘Les based framework for transient instability prediction and mitigation using PMU data’, IET Gener. Transm. Distrib., 2016, 10, (14), pp. 34313440.
    18. 18)
      • 18. Prakash, T., Kumar, V.S.: ‘Novel approach for signal selection and optimal control in emerging wide area monitored systems’. IEEE Power Electronics, Intelligent Control and Energy Systems (ICPEICES), July 2016, pp. 16.
    19. 19)
      • 19. Zhang, C., Jia, Y., Xu, Z.: ‘Optimal PMU placement for voltage control’. IEEE Smart Grid Communication, November 2016, pp. 747751.
    20. 20)
      • 20. Lu, C., Wang, Z.: ‘A cluster-autonomous partitioning algorithm in electrical power grid using Monte Carlo simulation’, Int. J. Model. Simul. Sci. Comput., 2017, 08, (04), p. 1750053.
    21. 21)
      • 21. Avramovic, B., Kokotovic, P.V., Winkelman, J.R., et al: ‘Area decomposition for electromechanical models of power systems’, Automatica, 1980, 16, (6), pp. 637648.
    22. 22)
      • 22. Diestel, R.: ‘Graph theory’ (Springer-Verlag, New York, 2000).
    23. 23)
      • 23. Fu, C., Bose, A.: ‘Contingency ranking based on severity indices in dynamic security analysis’, IEEE Trans. Power Syst., 1999, 14, (3), pp. 980985.
    24. 24)
      • 24. Tiwari, A., Ajjarapu, V.: ‘Event identification and contingency assessment for voltage stability via PMU’. 39th North America Power Symp. (NAPS ‘07), September 2007, pp. 413420.
    25. 25)
      • 25. Milano, F.: ‘Power system analysis toolbox (PSAT), version 2.1.8,http://faraday1.ucd.ie/psat.html, accessed 10 June 2017.
    26. 26)
      • 26. Genetic Algorithms Toolbox, version 1.2.: Complex Optimization and Decision Making Laboratory (CODeM), http://codem.group.shef.ac.uk/index.php/ga-toolbox, accessed June 10, 2017.
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