Sensitive Constrained Optimal PMU Allocation with Complete Observability for State Estimation Solution

Authors

  • R. Manam Electrical and Electronics Engineering Department, Jawaharlal Nehru Technological University, Kakinada, Kakinada, India
  • S. R. Rayapudi Electrical and Electronics Engineering Department, Jawaharlal Nehru Technological University, Kakinada, Kakinada, India
Volume: 7 | Issue: 6 | Pages: 2240-2250 | December 2017 | https://doi.org/10.48084/etasr.1542

Abstract

In this paper, a sensitive constrained integer linear programming approach is formulated for the optimal allocation of Phasor Measurement Units (PMUs) in a power system network to obtain state estimation. In this approach, sensitive buses along with zero injection buses (ZIB) are considered for optimal allocation of PMUs in the network to generate state estimation solutions. Sensitive buses are evolved from the mean of bus voltages subjected to increase of load consistently up to 50%. Sensitive buses are ranked in order to place PMUs. Sensitive constrained optimal PMU allocation in case of single line and no line contingency are considered in observability analysis to ensure protection and control of power system from abnormal conditions. Modeling of ZIB constraints is included to minimize the number of PMU network allocations. This paper presents optimal allocation of PMU at sensitive buses with zero injection modeling, considering cost criteria and redundancy to increase the accuracy of state estimation solution without losing observability of the whole system. Simulations are carried out on IEEE 14, 30 and 57 bus systems and results obtained are compared with traditional and other state estimation methods available in the literature, to demonstrate the effectiveness of the proposed method.

Keywords:

sensitive, bus, observability, phasor, measurement, pmu, state, estimation, zero, injection, zib

Downloads

Download data is not yet available.

References

K. E. Martin, D. Hamai, M. G. Adamiak, S. Anderson, M. Begovic, G. Benmouyal, G. Brunello, J. Burger, J. Y. Cai, B. Dickerson, V. Gharpure, B. Kennedy, D. Karlsson, A. G. Phadke, J. Salj, V. Skendzic, J. Sperr, Y. Song, C. Huntley, B. Kasztenny, E. Price, “Exploring the IEEE Standard C37.118-2005 synchrophasors for power systems”, IEEE Transactions on Power Delivery, Vol. 23, No. 4, pp. 1805-1811, 2008 DOI: https://doi.org/10.1109/TPWRD.2007.916092

L. L. Lai, H. T. Zhang, S. Mishra, D. Ramasubramanian, C. S. Lai, F. Y. Xu, “Lessons Learned from July 2012 Indian Blackout”, 9th IET International Conference on Advances in Power System Control, Operation and Management, pp. 1-6, 2012

T. Moger, T. Dhadbanjan, “A novel index for identification of weak nodes for reactive compensation to improve voltage stability IET Generation, Transmission & Distribution, Vol. 9, No. 14, pp. 1826-1834, 2015 DOI: https://doi.org/10.1049/iet-gtd.2015.0054

F. Capitanescu, T. V. Cutsem, “Unified sensitivity analysis of unstable or low voltages caused by load increases or contingencies”, IEEE Transactions on Power Systems, Vol. 20, No. 1, pp. 321-329, 2005. DOI: https://doi.org/10.1109/TPWRS.2004.841243

V. Balamourougan, T. S. Sidhu, M. S. Sachdev, “Technique for online prediction of voltage collapse IEE Proceedings - Generation, Transmission and Distribution, Vol. 151, No. 4, pp. 453-460, 2004 DOI: https://doi.org/10.1049/ip-gtd:20040612

I. Musirin, T. K. A. Rahman, “On-line Voltage stability based contingency ranking using fast voltage stability index (FVSI)”, Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES,Vol. 2, pp. 1118-1123, 2002

B. Divya, R. Devarapalli, “Estimation of sensitive node for IEEE-30 bus system by load variation”, Green Computing Communication and Electrical Engineering, pp. 1-4, 2014 DOI: https://doi.org/10.1109/ICGCCEE.2014.6922357

A. G. Phadke, J. S. Thorp, K. J. Karimi, “State estimation with phasor measurements”, IEEE Power Engineering Review, Vol. PER-6, No. 2, pp. 48, 1986 DOI: https://doi.org/10.1109/MPER.1986.5528179

A.i Abur, F. H. Magnago, “Optimal meter placement for maintaining observability during single branch outages”, IEEE Transactions on Power Systems, Vol. 14, No. 4, pp. 1273–1278, 1999 DOI: https://doi.org/10.1109/59.801884

F. Aminifar, C. Lucas, A. Khodaei, M. Fotuhi-Firuzabad, “Optimal placement of phasor measurement units using immunity genetic algorithm”, IEEE Transactions on Power Delivery, Vol. 24, No. 3, pp. 1014-1020, 2009 DOI: https://doi.org/10.1109/TPWRD.2009.2014030

B. Milosevic, M. Begovic, “Nondominated sorting genetic algorithm for optimal phasor measurement placement”, IEEE Transactions on Power Systems, Vol. 18, No.1, pp. 69-75, 2003 DOI: https://doi.org/10.1109/TPWRS.2002.807064

K. G. Khajeh, E. Bashar, A. M. Rad, G. B. Gharehpetian, “Integrated model considering effects of zero Injection buses and conventional measurements on optimal PMU placement”, IEEE Transactions on Smart Grid, Vol. 8 , No. 2, pp. 1006-1013, 2017

D. Dua, S. Dambhare, R. K. Gajbhiye, S. A. Soman, “Optimal Multistage Scheduling of PMU Placement: An ILP Approach”, IEEE transactions on Power Delivery, Vol. 23, No. 4, pp. 1812-1820, 2008 DOI: https://doi.org/10.1109/TPWRD.2008.919046

B. Gou, “Generalized Integer Linear Programming formulation for optimal PMU placement”, IEEE Transactions on Power Systems, Vol. 23, No. 3, pp. 1099-1104, Aug. 2008 DOI: https://doi.org/10.1109/TPWRS.2008.926475

N. M. Manousakis, G. N. Korres, “A weighted least squares algorithm for optimal PMU placement”, IEEE Transactions on Power Systems, Vol. 28, No. 3 pp. 3499-3500, 2013 DOI: https://doi.org/10.1109/TPWRS.2013.2242698

S. Chakrabarti, E. Kyriakides, “Optimal placement of phasor measurement units for power system observability”, IEEE Transactions on Power Systems, Vol. 23, No. 3, pp. 1433–1440, 2008 DOI: https://doi.org/10.1109/TPWRS.2008.922621

S. Chakrabarti, E. Kyriakides, D. G. Eliades, “Placement of synchronized measurements for power system observability”, IEEE Transactions on Power Systems, Vol. 24, No. 1, pp. 12–19, 2009 DOI: https://doi.org/10.1109/TPWRD.2008.2008430

N. H. Abbasy, H. M Ismail, “A unified approach for the optimal PMU location for power system state estimation”, IEEE Transactions on Power Systems, Vol. 24, No. 2, pp. 806–813, 2009 DOI: https://doi.org/10.1109/TPWRS.2009.2016596

G. Valverde, S. Chakrabarti, E. Kyriakides, V. Terzija, “A constrained formulation of hybrid state estimation”, IEEE Transactions on Power Systems, Vol. 26, No. 3, pp. 1102-1109, 2011 DOI: https://doi.org/10.1109/TPWRS.2010.2079960

A. Simoes Costa, A. Albuquerque, D. Bez, “An estimation fusion method for including phasor measurements in to power system”, IEEE Transactions on Power Systems, Vol. 28, No. 2, pp. 1910-1920, 2013 DOI: https://doi.org/10.1109/TPWRS.2012.2232315

S. Chakrabarti, E. Kyriakides, G. Ledwich, A. Ghosh, “Inclusion of PMU current phasor measurements in a power system state estimator”, IET Generation, Transmission & Distribution, Vol. 4, No. 10, pp. 1104-1115, 2009 DOI: https://doi.org/10.1049/iet-gtd.2009.0398

A. Abur, A. G. Exposito, Power system sate estimation: Theory and Implementations, Taylor & Francis, 2004 DOI: https://doi.org/10.1201/9780203913673

A. G. Padke, J. S. Thorp, “Synchrophasor Measurements and their Applications”, Power electronics and power systems, Springer, 2008

B. Xu, A. Abur, “Observability analysis and measurement placement for systems with PMUs”, IEEE PES. Power systems conference and Exposition, Vol. 2, pp. 943-946, 2004

C. Bruno, C. Candia, L. Franchi, G. Giannuzzi, M. Pozzi, R. Zaottini, M. Zaramella, “Possibility of enhancing classical weighted least squares state estimation with linear PMU measurements”, IEEE Bucharest Power Tech, pp. 2–7, 2009 DOI: https://doi.org/10.1109/PTC.2009.5281817

R. F. Nuqui, A. G. Phadke, “Hybrid linear state estimation utilizing synchronized phasor measurements”, IEEE Lausanne Power Tech, pp. 1665-1669, 2007 DOI: https://doi.org/10.1109/PCT.2007.4538565

G. N. Korres, N. M. Manousakis, “State estimation and observability analysis for phasor measurement unit measured systems”, IET Generation, Transmission & Distribution, Vol. 6, No. 9, pp. 902-913, 2012 DOI: https://doi.org/10.1049/iet-gtd.2011.0492

J. Xu, M. H. F. Wen, V. O. K. Li, K. C. Leung, “Optimal PMU placement for wide-area monitoring using chemical reaction optimization”, IEEE PES Innovative Smart Grid Technologies Conference, pp. 1-6, 2013

A. Ahmadi, Y. Alinejad-Beromi, M. Moradi, “Optimal PMU placement for power system observability using binary particle swarm optimization and considering measurement redundancy”, Expert Systems with Applications, Vol. 38, No. 6, pp. 7263-7269, 2011 DOI: https://doi.org/10.1016/j.eswa.2010.12.025

M. Ravindra, R. Srinivasa Rao. “Dynamic state estimation solution with optimal allocation of PMUs in presence of load changes”, IEEE International Conference on Intelligent Control Power and Instrumentation, pp. 163-168, 2016 DOI: https://doi.org/10.1109/ICICPI.2016.7859695

K. Jamuna, K. S. Swarup, “Multi-objective biogeography based optimization for optimal PMU placement”, Applied Soft Computing, Vol. 12, No.5, pp. 1503-1510, 2012 DOI: https://doi.org/10.1016/j.asoc.2011.12.020

Downloads

How to Cite

[1]
R. Manam and S. R. Rayapudi, “Sensitive Constrained Optimal PMU Allocation with Complete Observability for State Estimation Solution”, Eng. Technol. Appl. Sci. Res., vol. 7, no. 6, pp. 2240–2250, Dec. 2017.

Metrics

Abstract Views: 686
PDF Downloads: 231

Metrics Information
Bookmark and Share