State Estimation Solution with Optimal Allocation of Phasor Measurement Units Considering Zero Injection Bus Modeling
This paper presents state estimation with Phasor Measurement Unit (PMU) allocation to obtain complete observability of network. A matrix is designed with modeling of zero injection constraints to minimize PMU allocations. State estimation algorithm is developed with optimal allocation of PMUs to find accurate states of network. The incorporation of PMU into traditional state estimation process improves accuracy and computational performance for large power systems. The nonlinearity integrated with zero injection (ZI) constraints is remodeled to linear frame to optimize number of PMUs. The problem of optimal PMU allocation is regarded with modeling of ZI constraints, PMU loss or line outage, cost factor and redundant measurements. The proposed state estimation with optimal PMU allocation has been compared with traditional state estimation process to show its importance. MATLAB programming on IEEE 14, 30, 57, and 118 bus networks is implemented out by Binary Integer Programming (BIP) method and compared with other methods to show its effectiveness.
[1] A.G. Phadke and J.S. Thorp Synchronized Phasor Measurements and their Applications, New York: Springer, Ed.1, 2008.
[2] A. G. Phadke, J. S. Thorp, and K. J. Karimi, “State estimation with phasor measurements,” IEEE Trans. Power Syst., vol. 1, no. 1, pp.233–238, Feb. 198
[3] H. H. Muller and C. A. Castro, “Genetic Algorithm-based phasor measurement unit placement method considering observability and security criteria,” IET Generation Transmission and Distribution., vol. 10, no. 1, pp. 270-280, Jan. 2016.
[4] M. Nazari-Haris and B. Mohammadi-ivatloo, “Optimal placement of phasor measurement units to attain power system observability utilizing an upgraded binary harmony search algorithm,” Energy Syst. vol. 6, no. 2, pp. 201–220, jun. 2015.
[5] M. Dalali and H. K .Karegar, “Optimal PMU Placement for full observability of the power network with maximum redundancy using modified binary cuckoo optimization algorithm,” IET Generation Transmission and Distribution, vol. 10, no. 11, pp. 2817-2824, Aug. 2016.
[6] G.N. Korres, N. M. Manousakis, T. C. Xykis and J. Lofberg, “Optimal phasor measurement unit placement for numerical observability in the presence of conventional measurements using semi-definite programming,” IET Generation Transmission and Distribution, vol. 9, no. 15, pp. 2427-2436, Nov. 2015
[7] K. Jamuna and K.S Swarup, “Multi-objective biogeography based optimization for optimal PMU placement,” Appl. Soft Computing, vol. 12, no. 5, pp.1503–1510, May. 2012.
[8] X. Bian and J. Qiu “Adaptive clonal algorithm and its application for optimal PMU placement,” Proc. In Communications, Circuits and Systems, Int. Conf. IEEE, vol.3, pp.2102-2106, 2006
[9] A. Ahmadi, Y.Alinejad Beromi and M. Moradi, PMU placement for power system observability using binary particle swarm optimization and considering measurement redundancy,” Expert systems with application, vol. 38, no. 6, pp. 7263–7269, June. 2011.
[10] J X.u, M.H.F. Wen, V. O. K. Li, and Ka-Cheong Leung, “Optimal PMU placement for wide area monitoring using chemical reaction optimization,” Proc. Innovative smart grid technologies, Feb. 2013, pp. 1–6.
[11] G. Valverde, S. Chakrabarti, E. Kyriakides, and V. Terzija, “A constrained formulation of hybrid state estimation,” IEEE Trans. Power Syst., vol. 26, no. 3, pp. 1102-1109, Aug. 2011.
[12] A. S. Costa, A. Albuquerque, D. Bez, “An Estimation Fusion method for including phasor measurements into power system real time modeling,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 1910-1920, May 2013.
[13] S. Chakrabarti, Kyriakides, G. Ledwich, A. Ghosh, “Inculsion of current Phasor measurements in a power system state estimator,” IET generation transmission and distribution, vol. 4, no. 10, pp. 1104-1115, Oct 2010.
[14] A. Abur and A. G. Exposito, “Power System Sate Estimation: Theory and Implementations,” New York: Marcel Dekker, 2004.Ed.1.
[15] D. Dua, S. Dambhare, G. Rajeev Kumar, S. A. Soman, “Optimal Multistage Scheduling of PMU Placement: An ILP Approach,” IEEE transactions on power delivery, vol. 23, no. 4, oct 2008.
[1] A.G. Phadke and J.S. Thorp Synchronized Phasor Measurements and their Applications, New York: Springer, Ed.1, 2008.
[2] A. G. Phadke, J. S. Thorp, and K. J. Karimi, “State estimation with phasor measurements,” IEEE Trans. Power Syst., vol. 1, no. 1, pp.233–238, Feb. 198
[3] H. H. Muller and C. A. Castro, “Genetic Algorithm-based phasor measurement unit placement method considering observability and security criteria,” IET Generation Transmission and Distribution., vol. 10, no. 1, pp. 270-280, Jan. 2016.
[4] M. Nazari-Haris and B. Mohammadi-ivatloo, “Optimal placement of phasor measurement units to attain power system observability utilizing an upgraded binary harmony search algorithm,” Energy Syst. vol. 6, no. 2, pp. 201–220, jun. 2015.
[5] M. Dalali and H. K .Karegar, “Optimal PMU Placement for full observability of the power network with maximum redundancy using modified binary cuckoo optimization algorithm,” IET Generation Transmission and Distribution, vol. 10, no. 11, pp. 2817-2824, Aug. 2016.
[6] G.N. Korres, N. M. Manousakis, T. C. Xykis and J. Lofberg, “Optimal phasor measurement unit placement for numerical observability in the presence of conventional measurements using semi-definite programming,” IET Generation Transmission and Distribution, vol. 9, no. 15, pp. 2427-2436, Nov. 2015
[7] K. Jamuna and K.S Swarup, “Multi-objective biogeography based optimization for optimal PMU placement,” Appl. Soft Computing, vol. 12, no. 5, pp.1503–1510, May. 2012.
[8] X. Bian and J. Qiu “Adaptive clonal algorithm and its application for optimal PMU placement,” Proc. In Communications, Circuits and Systems, Int. Conf. IEEE, vol.3, pp.2102-2106, 2006
[9] A. Ahmadi, Y.Alinejad Beromi and M. Moradi, PMU placement for power system observability using binary particle swarm optimization and considering measurement redundancy,” Expert systems with application, vol. 38, no. 6, pp. 7263–7269, June. 2011.
[10] J X.u, M.H.F. Wen, V. O. K. Li, and Ka-Cheong Leung, “Optimal PMU placement for wide area monitoring using chemical reaction optimization,” Proc. Innovative smart grid technologies, Feb. 2013, pp. 1–6.
[11] G. Valverde, S. Chakrabarti, E. Kyriakides, and V. Terzija, “A constrained formulation of hybrid state estimation,” IEEE Trans. Power Syst., vol. 26, no. 3, pp. 1102-1109, Aug. 2011.
[12] A. S. Costa, A. Albuquerque, D. Bez, “An Estimation Fusion method for including phasor measurements into power system real time modeling,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 1910-1920, May 2013.
[13] S. Chakrabarti, Kyriakides, G. Ledwich, A. Ghosh, “Inculsion of current Phasor measurements in a power system state estimator,” IET generation transmission and distribution, vol. 4, no. 10, pp. 1104-1115, Oct 2010.
[14] A. Abur and A. G. Exposito, “Power System Sate Estimation: Theory and Implementations,” New York: Marcel Dekker, 2004.Ed.1.
[15] D. Dua, S. Dambhare, G. Rajeev Kumar, S. A. Soman, “Optimal Multistage Scheduling of PMU Placement: An ILP Approach,” IEEE transactions on power delivery, vol. 23, no. 4, oct 2008.
@article{"International Journal of Electrical, Electronic and Communication Sciences:78141", author = "M. Ravindra and R. Srinivasa Rao and V. Shanmukha Naga Raju", title = "State Estimation Solution with Optimal Allocation of Phasor Measurement Units Considering Zero Injection Bus Modeling", abstract = "This paper presents state estimation with Phasor Measurement Unit (PMU) allocation to obtain complete observability of network. A matrix is designed with modeling of zero injection constraints to minimize PMU allocations. State estimation algorithm is developed with optimal allocation of PMUs to find accurate states of network. The incorporation of PMU into traditional state estimation process improves accuracy and computational performance for large power systems. The nonlinearity integrated with zero injection (ZI) constraints is remodeled to linear frame to optimize number of PMUs. The problem of optimal PMU allocation is regarded with modeling of ZI constraints, PMU loss or line outage, cost factor and redundant measurements. The proposed state estimation with optimal PMU allocation has been compared with traditional state estimation process to show its importance. MATLAB programming on IEEE 14, 30, 57, and 118 bus networks is implemented out by Binary Integer Programming (BIP) method and compared with other methods to show its effectiveness.
", keywords = "Observability, phasor measurement units, synchrophasors, SCADA measurements, zero injection bus.", volume = "12", number = "11", pages = "788-6", }
