American Journal of Electrical Power and Energy Systems

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Decomposition-Coordination Model and Algorithm for Parallel Calculation of Power System State Estimation Problem

Received: 16 November 2014    Accepted: 04 December 2014    Published: 08 December 2014
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Abstract

Power system state estimation is the process of computing a reliable estimate of the system state vector composed of bus voltages’ magnitudes and angles from telemetered measurements on the system. This estimate of the state vector provides the description of the system necessary for operation, security monitoring and control. Many methods are described in literature for solving the state estimation problem, the most important of which are the classical weighted least squares and the non-quadratic method. However, both showed drawbacks when it comes to application to large-scale power system networks. In this paper, a new method in the name of decomposition-coordination approach using the weighted least squares is introduced in solving the large-scale power system state estimation problem. The estimation criterion is reformulated; voltage measurement, real and reactive power injections, real and reactive power flows, and real and reactive power flows in tie-line models of a decomposed system are developed. Two level structure of solving the estimation problem is introduced. The first level solves the sub-problem using gradient procedure methods while the second level determines the interconnection variables using predictive method. The positive characteristic of the method is that the coordinator has little work of predicting interconnection variables instead of solving the state estimation problem. The method can be used to solve a multi-area state estimation using parallel or distributed processing architectures.

DOI 10.11648/j.epes.20140306.12
Published in American Journal of Electrical Power and Energy Systems (Volume 3, Issue 6, November 2014)
Page(s) 107-118
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Power Systems, Modelling of Measurement Data, State Estimation, Decomposition-Coordination Method, Algorithm

References
[1] J. Gu, K.A. Clements, G. Krumpholz, P.Davis, “ The solution of ill-conditioned Power System State Estimation Problem via the method of Peters and Wilkinson” Power Industry Computer Applications Conference Proceedings, Huston, May 1983, pp.239-246
[2] A.Monticelli, “State Estimation in Electric Power Systems: A generalized Approach, Kluweri Academic Publishers, Boston, 1999.
[3] A.Bose, and K.A. Clements, “Real-Time modelling of Power Networks”, IEEE Proceeding, Special issue on Computers in Power System Operations, Vol., 75, No. 12, Dec 1987, pp. 1607-1622
[4] B. Stott, O. Alsac and A. Monticelli, “Security Analysis and Optimization”, IEEE Proceeding, Vol. 75, No.12, Dec 1987, pp. 1623-1644
[5] Dy Liacco, “System security: The computer role.” IEEE Spectrum, Vol.16, No. 6, June 1978, pp. 48-53
[6] A.A. Hossam-Eldin, E.N. Abdallah and M.S.El-Nozahy, “A modified Genetic Based Technique for solving the Power System State Estimation Problem,” World Academy of Science, Engineering and Technology, Vol.55, 2009, pp. 311-320.
[7] J.B. Calvalho and F.M. Barbosa, “A parallel Algorithm to Power System State Estimation ,” IEEE, 1988, pp. 1213-1217
[8] O. Alsac, N. Vempati, B. Stott, and A. Monticelli, “Generalized State Estimation,” IEEE Transactions on Power Systems, Vol. 13, No.3, August 1998, pp. 1069-1075
[9] F.C. Schweppe, J. Wildes, and D. Rom, “PowerSystemStaticState Estimation Parts: I, II and III,” Power Industry Computer Conference, PICA, Denver, Colorado, June 1969.
[10] K.A. Clements and B.F. Wollenberg, “An algorithm for Observability Determination in Power System State Estimation,” Paper A75 447-3, IEEE/PES Summer Meeting, San Francisco, CA, July 1975.
[11] P. Zarco and A.G. Exposito, “Power System Parameter Estimation: A survey,” IEEE Trans. on Power Systems, Vol. 15, No.1, Feb. 2000, pp. 216-222
[12] Th. VanCutsem, J.L. Howard, M. Ribben-Pavella and Y.M. El-Fattah, “Hierarchical State Estimation,” International Journal Of Electric Power and Energy Systems, Vol. 2, April 1980, pp. 70-78
[13] Th. VanCutsem, J.L. Howard, and M. Ribben-Pavella, “A Two-level Static State Estimation for Electric Power Systems,” IEEE Trans. on PAS, Vol. PAS, August 1981, pp. 3722-3732
[14] M.Y. Patel and A.A. Girgis, “Two level State Estimation for Multi-area Power System,” IEEE Power Engineering Society General Meeting, June 2007
[15] S. Lakshminarasimhan, and A.A. Girgis, “Hierarchical State Estimation Applied to Wide-Area Power Systems,” IEEE Power Engineering Society General Meeting, June 2000, pp. 1-6.
[16] J.A. Aguado, C.P. Molina and V.H. Quintana, “Decentralized PowerSystemState Estimation: A Decomposition-Coordination Approach,2 IEEE Porto Power Tech. Conference, 10th -13th September, 2001, Porto, Portugal, Vol. 3, pp. 6-11
[17] M. Singh and M. Titli, “System: decomposition, Optimization and Control, Kluweri Publishers, Boston, 1978
[18] M.B. Couto Filho, A.M. Leite da Silva and D.M. Falcao, “Bibliography on Power System State Estimation (1968-1989)”, IEEE Trans. on PWR, Vol. 5, No.3, August 1990, pp.950-961
[19] R.D. Masiello and F.C. Schweppe, “A tracking Static State Estimator,” IEEE, Vol. PAS-90, March/April 1971, pp.1025-1033
[20] M.A. Kusekwa, “Real-Time State Estimation of a Distributed Electrical Power System Under Conditions of Deregulation,” PhD Thesis in Electrical Engineering, Cape Peninsula University of Technology, Cape Town, May 2010.
[21] http://www.mathworld.wolfram.com/Norm.html
Author Information
  • Electrical Engineering department, Dar es Salaam Institute of Technology (DIT), Dar es Salaam-Tanzania

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    Mashauri Adam Kusekwa. (2014). Decomposition-Coordination Model and Algorithm for Parallel Calculation of Power System State Estimation Problem. American Journal of Electrical Power and Energy Systems, 3(6), 107-118. https://doi.org/10.11648/j.epes.20140306.12

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    Mashauri Adam Kusekwa. Decomposition-Coordination Model and Algorithm for Parallel Calculation of Power System State Estimation Problem. Am. J. Electr. Power Energy Syst. 2014, 3(6), 107-118. doi: 10.11648/j.epes.20140306.12

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    AMA Style

    Mashauri Adam Kusekwa. Decomposition-Coordination Model and Algorithm for Parallel Calculation of Power System State Estimation Problem. Am J Electr Power Energy Syst. 2014;3(6):107-118. doi: 10.11648/j.epes.20140306.12

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  • @article{10.11648/j.epes.20140306.12,
      author = {Mashauri Adam Kusekwa},
      title = {Decomposition-Coordination Model and Algorithm for Parallel Calculation of Power System State Estimation Problem},
      journal = {American Journal of Electrical Power and Energy Systems},
      volume = {3},
      number = {6},
      pages = {107-118},
      doi = {10.11648/j.epes.20140306.12},
      url = {https://doi.org/10.11648/j.epes.20140306.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.epes.20140306.12},
      abstract = {Power system state estimation is the process of computing a reliable estimate of the system state vector composed of bus voltages’ magnitudes and angles from telemetered measurements on the system. This estimate of the state vector provides the description of the system necessary for operation, security monitoring and control. Many methods are described in literature for solving the state estimation problem, the most important of which are the classical weighted least squares and the non-quadratic method. However, both showed drawbacks when it comes to application to large-scale power system networks. In this paper, a new method in the name of decomposition-coordination approach using the weighted least squares is introduced in solving the large-scale power system state estimation problem. The estimation criterion is reformulated; voltage measurement, real and reactive power injections, real and reactive power flows, and real and reactive power flows in tie-line models of a decomposed system are developed. Two level structure of solving the estimation problem is introduced. The first level solves the sub-problem using gradient procedure methods while the second level determines the interconnection variables using predictive method. The positive characteristic of the method is that the coordinator has little work of predicting interconnection variables instead of solving the state estimation problem. The method can be used to solve a multi-area state estimation using parallel or distributed processing architectures.},
     year = {2014}
    }
    

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    JF  - American Journal of Electrical Power and Energy Systems
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    AB  - Power system state estimation is the process of computing a reliable estimate of the system state vector composed of bus voltages’ magnitudes and angles from telemetered measurements on the system. This estimate of the state vector provides the description of the system necessary for operation, security monitoring and control. Many methods are described in literature for solving the state estimation problem, the most important of which are the classical weighted least squares and the non-quadratic method. However, both showed drawbacks when it comes to application to large-scale power system networks. In this paper, a new method in the name of decomposition-coordination approach using the weighted least squares is introduced in solving the large-scale power system state estimation problem. The estimation criterion is reformulated; voltage measurement, real and reactive power injections, real and reactive power flows, and real and reactive power flows in tie-line models of a decomposed system are developed. Two level structure of solving the estimation problem is introduced. The first level solves the sub-problem using gradient procedure methods while the second level determines the interconnection variables using predictive method. The positive characteristic of the method is that the coordinator has little work of predicting interconnection variables instead of solving the state estimation problem. The method can be used to solve a multi-area state estimation using parallel or distributed processing architectures.
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