Power system state estimation is an effective online tool for monitoring, control and for providing consistent database in energy management systems. This paper presents an algorithm for state estimation of the Tanzanian power system network using a non-quadratic state criterion. Equality and inequality constraints existing in a power system are included in formulating the estimation problem. Equality constraints are target values used in load flow analysis and are included in power system state estimation in order to restore observability to those parts of the power system network which are permanently or temporarily unobservable. Inequality constraints are limits such as minimum and maximum reactive power generation, transformer tap and phase-shift. The solution techniques used is primal-dual interior point logarithmic barrier functions to treat the inequality constraints. An algorithm is developed using the method and a program coded in MATLAB is applied in implementing the simulation. Computational issues arising in the implementation of the algorithm are presented. The simulation results demonstrate that the primal-dual logarithmic barrier interior point algorithm is a useful numerical tool to compute the state of an electrical power system network. The inequality constraints play essential role in enhancing the reliability of the estimation results. Also, it is expected that significant benefit could be gained from application of the constrained state estimation algorithm to the Tanzanian power system network.
| Published in | International Journal of Energy and Power Engineering (Volume 3, Issue 5) |
| DOI | 10.11648/j.ijepe.20140305.18 |
| Page(s) | 266-276 |
| 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 |
Power Systems, Non-Quadratic State Estimation, Simulation, Interior Point Method, MATLAB Program
| [1] | F.F. Wu. Real-Time network security monitoring, assessment and optimization, 9th Power Systems Computation Conference,Cascais, Portugal,31 August-4 September 1987: 83-100. |
| [2] | A. Monticelli. State estimation in Electric Power Systems: A generalized approach. Kluwer Academic Publishers; Boston, 1999. |
| [3] | N.H. Abbasy and S.M. Shahidehpour. Application of Non-linear Programming in Power System State Estimation, Electric Power Systems Research, 12, 1987:41-50. |
| [4] | R.E. Larson, W.F. Tinney, and J. Peschon, State Estimation in Power Systems, part I: Theory and feasibility. IEEE Transactions on Power Apparatus & Systems; PAS-89(3), 1970: 345-352 |
| [5] | F.C. Schweppe and J.Wildes. Power system Static State Estimation part I: exact model, IEEE Transactions on Power Apparatus & Systems; Vol. PAS-89(1); 1970: 120-125 |
| [6] | F.C. Schweppe and D.B. Rom. Power System Static State Estimation, part II: approximate model, IEEE Transactions on Power Apparatus & Systems; Vo. PAS-89, (1), 1970: 125-130 |
| [7] | F.C. Schweppe, Power System Static State Estimation part III: implementation, IEEE Transaction on Power Apparatus & Systems; Vol. PAS-89(1), 1970: 130-135 |
| [8] | J.J. Allemong, L.Radu, and A.M. Sasson. A fast and reliable state estimation algorithm for AEP’s new control centre, IEEE Transactions on Power Apparatus & Systems; Vol. PAS-101(3), 1982: 933-944 |
| [9] | L. Holten, A. Gjelsvick, S. Aam, F.F. Wu, and W.H.E. Liu, Comparison of different methods for state estimation, IEEE Transaction on Power Systems; 3, (4), 1988: 1798-1806 |
| [10] | A. Abur and A.G. Exposito, Power System State Estimation-Theory and Implementations. New York: Marcel Dekker, 2004 |
| [11] | A. Abur and M. K. Celik, Least Absolute Value State Estimation with Equality and Inequality Constraints, IEEE Transactions on Power Systems; 8, (2), 1993: 680-686 |
| [12] | A. Abur and M.K. Celik, A fast algorithm for the Weighted Least Absolute Value State Estimation, IEEE Transactions on Power Systems; 6, (1), 1991: 1-8 |
| [13] | K.A. Clements, P.W. Davis, and K.D. Frey, An interior Point Algorithm for Weighted Least Absolute Value Power System State Estimation; IEEE/PES, 91-WM 235-2 PWRS, New York, 1991 |
| [14] | H. Singh and F.L. Alvarado, Weighted least Absolute Value State Estimation using Interior Point Methods. IEEE Transactions on Power Systems; 9(3), 1994: 1478-1484 |
| [15] | H. Wei, H. Sasaki, J. Kubokawa, and R. Yokoyama, An interior point method for power system weighted non-linear L1 norm static state estimation. IEEE Transaction on Power Systems; 13(2), 1998: 17-23 |
| [16] | http://www.tanesco.com (May 2012) |
| [17] | http://www.nishati.go.tz (March 2012) |
| [18] | F.F. Wu, and A. Monticelli, A critical review on external network modelling for on-line security analysis. International Journal of Electrical Power Engineering Systems, Vol.3, 1983: 222-235 |
| [19] | J.A. Momoh, Electric Power System Applications of Optimization, CRC Press, Taylor & Francis Group, New York, 2009 |
| [20] | K.A. Clements, P.W. Davis, and K.D. Frey, Treatment of inequality constraints in power system state estimation, IEEE Transactions on Power Systems, Vol. 10, 1995: 567-573 |
| [21] | A.V. Fiacco and G.P. McCormick, Non-linear Programming: Sequential Unconstrained Minimization Technique, John Wiley & Sons, New York, 1968 |
| [22] | N.K. Karmarkar, A new polynomial-time algorithm for linear programming, Combinatorica 4, 1984: 183-295. |
| [23] | K.A. Clements, P.W. Davis, and K.D. Frey, An efficient algorithm for computing the Weighted Least Absolute Value Estimation in Power System State Estimation, “Proceeding of IFAC int. Symposium on Power System and Power Plant Control, Seoul Korea, August 22-25, 1989: 785-790 |
| [24] | K. Chiite and K.S. Swarup, Power System State Estimation using IP Barrier method, department of Electrical Engineering, Indian Institute of Technology Madras, 600036 INDIA, 2003: 1-6 |
| [25] | Information on norms obtained from http://www.mathworld.wolfram.com/Norm.html |
| [26] | http://www.ee.washington.edu/research/pstca |
APA Style
Mashauri Adam Kusekwa. (2014). State Estimation of the Tanzanian Power System Network Using Non-Quadratic Criterion and MATLAB Environment. International Journal of Energy and Power Engineering, 3(5), 266-276. https://doi.org/10.11648/j.ijepe.20140305.18
ACS Style
Mashauri Adam Kusekwa. State Estimation of the Tanzanian Power System Network Using Non-Quadratic Criterion and MATLAB Environment. Int. J. Energy Power Eng. 2014, 3(5), 266-276. doi: 10.11648/j.ijepe.20140305.18
AMA Style
Mashauri Adam Kusekwa. State Estimation of the Tanzanian Power System Network Using Non-Quadratic Criterion and MATLAB Environment. Int J Energy Power Eng. 2014;3(5):266-276. doi: 10.11648/j.ijepe.20140305.18
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ijepe.20140305.18,
author = {Mashauri Adam Kusekwa},
title = {State Estimation of the Tanzanian Power System Network Using Non-Quadratic Criterion and MATLAB Environment},
journal = {International Journal of Energy and Power Engineering},
volume = {3},
number = {5},
pages = {266-276},
doi = {10.11648/j.ijepe.20140305.18},
url = {https://doi.org/10.11648/j.ijepe.20140305.18},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20140305.18},
abstract = {Power system state estimation is an effective online tool for monitoring, control and for providing consistent database in energy management systems. This paper presents an algorithm for state estimation of the Tanzanian power system network using a non-quadratic state criterion. Equality and inequality constraints existing in a power system are included in formulating the estimation problem. Equality constraints are target values used in load flow analysis and are included in power system state estimation in order to restore observability to those parts of the power system network which are permanently or temporarily unobservable. Inequality constraints are limits such as minimum and maximum reactive power generation, transformer tap and phase-shift. The solution techniques used is primal-dual interior point logarithmic barrier functions to treat the inequality constraints. An algorithm is developed using the method and a program coded in MATLAB is applied in implementing the simulation. Computational issues arising in the implementation of the algorithm are presented. The simulation results demonstrate that the primal-dual logarithmic barrier interior point algorithm is a useful numerical tool to compute the state of an electrical power system network. The inequality constraints play essential role in enhancing the reliability of the estimation results. Also, it is expected that significant benefit could be gained from application of the constrained state estimation algorithm to the Tanzanian power system network.},
year = {2014}
}
TY - JOUR T1 - State Estimation of the Tanzanian Power System Network Using Non-Quadratic Criterion and MATLAB Environment AU - Mashauri Adam Kusekwa Y1 - 2014/11/21 PY - 2014 N1 - https://doi.org/10.11648/j.ijepe.20140305.18 DO - 10.11648/j.ijepe.20140305.18 T2 - International Journal of Energy and Power Engineering JF - International Journal of Energy and Power Engineering JO - International Journal of Energy and Power Engineering SP - 266 EP - 276 PB - Science Publishing Group SN - 2326-960X UR - https://doi.org/10.11648/j.ijepe.20140305.18 AB - Power system state estimation is an effective online tool for monitoring, control and for providing consistent database in energy management systems. This paper presents an algorithm for state estimation of the Tanzanian power system network using a non-quadratic state criterion. Equality and inequality constraints existing in a power system are included in formulating the estimation problem. Equality constraints are target values used in load flow analysis and are included in power system state estimation in order to restore observability to those parts of the power system network which are permanently or temporarily unobservable. Inequality constraints are limits such as minimum and maximum reactive power generation, transformer tap and phase-shift. The solution techniques used is primal-dual interior point logarithmic barrier functions to treat the inequality constraints. An algorithm is developed using the method and a program coded in MATLAB is applied in implementing the simulation. Computational issues arising in the implementation of the algorithm are presented. The simulation results demonstrate that the primal-dual logarithmic barrier interior point algorithm is a useful numerical tool to compute the state of an electrical power system network. The inequality constraints play essential role in enhancing the reliability of the estimation results. Also, it is expected that significant benefit could be gained from application of the constrained state estimation algorithm to the Tanzanian power system network. VL - 3 IS - 5 ER -
Information
Email: