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CHARACTERIZATION OF STATE ESTIMATION BIASES

Published online by Cambridge University Press:  12 December 2005

A. P. Sakis Meliopoulos  and
George K. Stefopoulos
A. P. Sakis Meliopoulos
Affiliation:
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, E-mail: [email protected]
George K. Stefopoulos
Affiliation:
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, E-mail: [email protected]
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Abstract

The control and operation of an electric power system is based on the ability to determine the state of the system in real time. State estimation (SE) has been introduced in the 1960s to achieve this objective. The initial implementation was based on single-phase measurements and a power system model that was assumed to operate under single-frequency, balanced conditions, and a symmetric system model. These assumptions are still prevalent today. The single-frequency, balanced, and symmetric system assumptions have simplified the implementation but have generated practical problems. The experience is that the SE problem does not have 100% performance; that is, there are cases and time periods for which the SE algorithm will not converge. There are practical and theoretical reasons for this and they are explained in the paper. Recent mergers and mandated regional transmission organizations (RTOs) as well as recent announcements for the formation of mega-RTOs will result in the application of the SE in systems of unprecedented size. We believe that these practical and theoretical issues will become of greater importance. There are scientists who believe that the SE problem is scalable, meaning that it will work for the mega-RTOs the same way that it performs now for medium–large systems. There are scientists who believe that this is not true. The fact is that no one has investigated the problem, let alone performed numerical experiments to prove or disprove any claims. This paper identifies a number of issues relative to the SE of mega-RTOs and provides some preliminary results from numerical experiments for the relation between the SE algorithm performance and the power system size.

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 20 , Issue 1 , January 2006 , pp. 157 - 174
Copyright
© 2006 Cambridge University Press

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References

REFERENCES

Alsac, O., Vempati, N., Stott, B., & Monticelli, A. (1998). Generalized state estimation. IEEE Transactions on Power Systems 13(3): 1069–1075.Google Scholar
Arifian, A., Ibrahim, M., Meliopoulos, S., & Zelingher, S. (1997). Optic technology monitors HV bus. Transmission and Distribution 49(5): 62–68.Google Scholar
Clements, K.A., Denison, O.J., & Ringlee, R.J. (1973). The effects of measurement nonsimultaneity, bias, and parameter uncertainty on power system state estimation. In 1973 PICA Conference Proceedings, pp. 327–331.
Fardanesh, B., Zelingher, S., Sakis Meliopoulos, A.P., Cokkinides, G., & Ingleson, J. (1998). Multifunctional synchronized measurement network. IEEE Computer Applications in Power 11(1): 26–30.Google Scholar
Hansen, C.W. & Debs, A.S. (1995). Power system state estimation using three-phase models. IEEE Transactions on Power Systems 10(2): 818–824.Google Scholar
Ingram, M., Bell, S., Matthews, S., Meliopoulos, A.P., & Cokkinides, G. (2004). Use of phasor measurements, SCADA and IED data to improve state estimation. In 7th Fault and Disturbance Analysis Conference.
Krumpholz, G.R., Clements, K.A., & Davis, P.W. (1980). Power system observability: A practical algorithm using network topology. IEEE Transactions on Power Apparatus and Systems 99(4): 1534–1542.Google Scholar
Meliopoulos, A.P., Zhang, F., Zelingher, S., Stillmam, G., Cokkinides, G.J., Coffeen, L., Burnett, R., & McBride, J. (1993). Transmission level instrument transformers and transient event recorders characterization for harmonic measurements. IEEE Transactions on Power Delivery 8(3): 1507–1517.Google Scholar
Phadke, A.G., Thorp, J.S., & Karimi, K.J. (1986). State estimation with phasor measurements. IEEE Transactions on Power Systems 1(1): 233–241.Google Scholar
Sakis Meliopoulos, A.P. (1998). Power system grounding and transients. New York: Marcel Dekker.
Sakis Meliopoulos, A.P., Cokkinides, G.C., & Webb, R.P. (1982). Multiphase power flow analysis. In Proceedings of Southeastcon, pp. 270–275.
Sakis Meliopoulos, A.P. & Papalexopoulos, A.D. (1986). Interpretation of soil resistivity measurements: Experience with the model SOMIP. IEEE Transactions on Power Delivery 1(4): 142–151.Google Scholar
Sakis Meliopoulos, A.P., Zhang, F., & Zelingher, S. (1992). Hardware and software requirements for a transmission system harmonic measurement system. In Proceedings of the Fifth International Conference on Harmonics in Power Systems (ICHPS V), pp. 330–338.
Sakis Meliopoulos, A.P., Zhang, F., & Zelingher, S. (1994). Power system harmonic state estimation. IEEE Transactions on Power Systems 9(3): 1701–1709.Google Scholar
Sakis Meliopoulos, A.P. & Zhang, F. (1996). Multiphase power flow and state estimation for power distribution systems. IEEE Transactions on Power Systems 11(2): 939–946.Google Scholar
Schweppe, F.C. & Wildes, J. (1970). Power system static-state estimation, Part I, II, and III. IEEE Transactions on Power Apparatus and Systems 89(1): 120–135.Google Scholar
Semlyen, A. & Deri, A. (1985). Time domain modeling of frequency dependent three-phase transmission line impedance. IEEE Transactions on Power Apparatus and Systems 104(6): 1549–1555.Google Scholar
Zelingher, S., Stillmann, G.I., & Sakis Meliopoulos, A.P. (1990). Transmission system harmonic measurement system: A feasibility study. In Proceedings of the Fourth International Conference on Harmonics in Power Systems (ICHPS IV), pp. 436–444.
Zhong, S. & Abur, A. (2002). Effects of non-transposed lines and unbalanced loads on state estimation. In Power Engineering Society Winter Meeting, vol 2, pp. 975–979.