OBSERVABILITY AND REDUNDANCY BASED PMU PLACEMENT AT OPTIMAL LOCATION OF POWER SYSTEM

Authors:

P. Lakshmi Narayana,M Venkatesan,

DOI NO:

https://doi.org/10.26782/jmcms.2019.12.00044

Keywords:

Branches,Observability,Phasor Measurement Units,Redundancy,Sequential Quadratic Programming(SQP),Zero Injection buses,

Abstract

This paper investigates redundancy and observability constrained Sequential Quadratic (SQ) technique for optimal Phasor Measurement Units (PMU) placement. The nonlinear constraints of buses are considered with this approach to optimize the quadratic objective for PMU placement. Zero Injection (ZI) bus constraints are modeled in quadratic formulation to less PMU locations. PMU placement with and without ZI constraints are compared to illustrate the importance of ZI constraint modeling for PMU placement. Redundancy in network is estimated with number of branches connected to bus. Redundancy of bus network is measured by the proposed Bus Redundancy Index (BRI). To estimate observability performance of the complete network, a Complete System Bus Observability Index (CSBOI) is proposed. IEEE- 14,30, and 57 bus systems are simulated with the proposed constrained SQ Programming formulation in MATLAB. The comparison of planned way with conventional methods is also considered to show its efficacy

Refference:

I. A. G. Phadke and J. S. Thorp, Synchronized phasor measurements and their
applications, vol. 1. New York, NY: Springer, 2008.
II. A. Abur, and A.G. Exposito, Power system state estimation: theory and
implementation. CRC press, 2004.
III. A. Almunif, and L.Fan, “Mixed Integer Linear Programming and Nonlinear
Programming for Optimal PMU Placement,” Power Symposium (NAPS),
North America, IEEE conference 2017.
IV. A.P.Singh, B.Nagu, N.V.Phanedra babu,and R.V. Jain, “Minimum
connectivity based technique for PMU placement in power systems,” 2017
8th International Conference on Computing, Communication and Networking
Technologies (ICCCNT). IEEE, 2017.
V. B.Gou. “Optimal placement of PMUs by integer linear programming,” IEEE
Transactions on power systems, vol. 23, no. 3, 2008, pp. 1525-1526.
VI. Babu, T. Vandana, T. Satyanarayana Murthy, and B. Sivaiah. “Detecting
unusual customer consumption profiles in power distribution systems—
APSPDCL.” 2013 IEEE International Conference on Computational
Intelligence and Computing Research. IEEE, 2013.
VII. Bulut, and M. Gol, “Binary integer programming based PMU placement in
the presence of conventional measurements,” PES Innovative Smart Grid
Technologies Conference Europe (ISGT-Europe), IEEE, 2016.
VIII. D. Dua, S. Dambhare, and G. Rajeev Kumar, “Optimal multistage scheduling
of PMU placement: An ILP approach”, IEEE Transactions on Power
Delivery, vol.23, no.4 ,2008, pp. 1812-1820
IX. F. Rashidi, A. Ebrahim, T. Niknam and M.R.Salehi, “Optimal placement of
PMUs with limited number of channels for complete topological
observability of power systems under various contingencies,” International
Journal of Electrical Power and Energy Systems, vol. 67, 2015, pp.125-137.
X. Gao, “An optimal PMU placement method considering bus weight and
voltage stability,” Environment and Electrical Engineering (EEEIC),12th
International Conference on. IEEE, 2013.
XI. Gómez, and A.R. Mario, “ILP-based multistage placement of PMUs with
dynamic monitoring constraints”, International Journal of Electrical Power &
Energy Systems, vol. 53, 2013, pp. 95-105.
XII. Gou, “Generalized integer linear programming formulation for optimal PMU
placement”, IEEE Transactions on Power Systems, vol. 23, no.3, 2008,
pp.1099-1104.
XIII. Kavitha, M., et al. “Evaluation of Antimitotic Activity of Mukia
maderaspatana L. Leaf Extract in Allium cepa Root Model.” International
Journal 4.1 (2014): 65-68.

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