A Genetic Algorithm Approach Considering Zero Injection Bus Constraint Modeling for Optimal Phasor Measurement Unit Placement
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A Genetic Algorithm Approach Considering Zero Injection Bus Constraint Modeling for Optimal Phasor Measurement Unit Placement

Authors: G. Chandana Sushma, T. R. Jyothsna

Abstract:

This paper presents optimal Phasor Measurement Unit (PMU) Placement in network using a genetic algorithm approach as it is infeasible and require high installation cost to place PMUs at every bus in network. This paper proposes optimal PMU allocation considering observability and redundancy utilizing Genetic Algorithm (GA) approach. The nonlinear constraints of buses are modeled to give accurate results. Constraints associated with Zero Injection (ZI) buses and radial buses are modeled to optimize number of locations for PMU placement. GA is modeled with ZI bus constraints to minimize number of locations without losing complete observability. Redundancy of every bus in network is computed to show optimum redundancy of complete system network. The performance of method is measured by Bus Observability Index (BOI) and Complete System Observability Performance Index (CSOPI). MATLAB simulations are carried out on IEEE -14, -30 and -57 bus-systems and compared with other methods in literature survey to show the effectiveness of the proposed approach.

Keywords: Constraints, genetic algorithm, observability, phasor measurement units, redundancy, synchrophasors, zero injection bus.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2021557

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References:


[1] A. Abur and A.G. Exposito, Power system state estimation: theory and implementation. CRC press, 2004.
[2] AG. Phadke and J.S. Thorp, Synchronized phasor measurements and their applications, vol. 1. New York, NY: Springer, 2008.
[3] B. Xu, and A. Abur, “Observability analysis and measurement placement for systems with PMUs,” Proc. Power Systems Conference and Exposition, 2004. IEEE PES, IEEE, 2004.
[4] B. Gou, “Generalized integer linear programming formulation for optimal PMU placement,” IEEE Transactions on Power Systems, vol. 23, no. 3, 2008, pp.1099-1104.
[5] S. Chakrabarti, E. Kyriakides and D.G. Eliades,“Placement of synchronized measurements for power system observability,” IEEE Transactions on Power Delivery, vol. 24, no. 1, 2009, pp. 12-19.
[6] B. Gou, “Optimal placement of PMUs by integer linear programming,” IEEE Transactions on power systems, vol. 23, no. 3, 2008, pp. 1525-1526.
[7] D. Dua, S. Dambhare, R.K.Gajbhiye and S.A.Soman, “Optimal multistage scheduling of PMU placement: An ILP approach,” IEEE Transactions on Power Delivery, vol. 23, no. 4, 2008, pp. 1812-1820.
[8] F. Rashidi, E. Abiri, 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 & Energy Systems, vol. 67, 2015, pp. 125-137.
[9] M. Ravindra and R. Srinivasa Rao, “Dynamic state estimation solution with optimal allocation of PMUs in presence of load changes,” Proc. Intelligent Control Power and Instrumentation (ICICPI), International Conference on. IEEE, 2016.
[10] S.A.Taher, H.nMahmoodi, and H. Aghaamouei, “Optimal PMU location in power systems using MICA,” Alexandria Engineering Journal, vol. 55, no. 1, 2016, pp. 399-406.
[11] R.A. El-Sehiemy, S.H. Aleem, A.Y. Abdelaziz and M.E. Balci, “A new fuzzy framework for the optimal placement of phasor measurement units under normal and abnormal conditions,” Resource-Efficient Technologies,vol. 3, no. 4, 2017, pp. 542-549.
[12] S. Li and Z. Meng, “Optimal PMU placement based on improved binary artificial bee colony algorithm,” Proc. Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2017 IEEE Conference and Exp. IEEE, 2017.
[13] M. Dalali, and H.K. Karegar, “Optimal PMU placement for full observability of the power network with maximum redundancy using modified binary cuckoo optimisation algorithm,” IET Generation, Transmission & Distribution, vol. 10, no. 11, 2016, pp. 2817-2824.
[14] A. Raj and C. Venkaiah, “Optimal PMU placement by teaching-learning based optimization algorithm,” proc. Systems Conference (NSC), 2015 39th National. IEEE 2015
[15] A. Ahmadi, Y. Alinejad-Beromi, and 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, 2011, pp. 7263-7269.
[16] P.K. Ghosh, and A. Tahabilder, “Optimal PMU placement for complete system observability and fault observability using graph theory,” proc. Electrical Engineering Congress (iEECON), 2017 International. IEEE, 2017.
[17] J.H. Holland, “Genetic algorithms,” Scientific american, vol. 267, no. 1 1992, pp. 66-73.
[18] 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.
[19] M.V. Khokhlov, A. Obushevs, I.Oleinikova and A. Mutule,”Optimal PMU placement for topological observability of power system: Robust measurement design in the space of phasor variables,” PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2016 IEEE. IEEE, 2016.
[20] A. Almunif, and L. Fan, “Mixed Integer Linear Programming and Nonlinear Programming for Optimal PMU Placement,” Power Symposium (NAPS), North America, IEEE conference 2017.
[21] X. Gao, “An optimal PMU placement method considering bus weight and voltage stability,” Environment and Electrical Engineering (EEEIC), 12th International Conference on. IEEE, 2013.
[22] 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.
[23] W. Hongyu, X. Cheng, and X. Zong, “Optimal PMU Placement for the System Observability Based on System Topology Model,” Trustworthy Systems and their Applications (TSA), 2016 Third International Conference on. IEEE, 2016.
[24] K. Jamuna and K. S. Swarup, “Multi-objective biogeography based optimization for optimal PMU placement”, Applied Soft Computing, vol. 12, no.5, 2012, pp.1503-1510.
[25] Allagui, B., H. Ben Aribia, and Hsan Hadj Abdallah. "Optimal placement of Phasor Measurement Units by genetic algorithm." Renewable Energies and Vehicular Technology (REVET), 2012 First International Conference on. IEEE, 2012.