Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
Optimization of Distributed Processors for Power System: Kalman Filters using Petri Net

Authors: Anant Oonsivilai, Kenedy A. Greyson

Abstract:

The growth and interconnection of power networks in many regions has invited complicated techniques for energy management services (EMS). State estimation techniques become a powerful tool in power system control centers, and that more information is required to achieve the objective of EMS. For the online state estimator, assuming the continuous time is equidistantly sampled with period Δt, processing events must be finished within this period. Advantage of Kalman Filtering (KF) algorithm in using system information to improve the estimation precision is utilized. Computational power is a major issue responsible for the achievement of the objective, i.e. estimators- solution at a small sampled period. This paper presents the optimum utilization of processors in a state estimator based on KF. The model used is presented using Petri net (PN) theory.

Keywords: Kalman filters, model, Petri Net, power system, sequential State estimator.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1076094

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 965

References:


[1] M. S. Grewal, and A. P. Andrews (2008), Kalman Filtering: Theory and Practice Using MATLAB, third Edition, John Wiley & Sons, Inc. 2008.
[2] F. Van der Heijden, R. P. W. Duin, D. de Ridder, D. M. J. Tax, (2004), Classification, Parameter Estimation and State Estimation, John Wiley & Sons, Ltd, 2004.
[3] E.J.Contreras-Hernandez, J. R. Cedeno-Maldonado (2006), "A Self- Adaptive Evolutionary Programming Approach for Power System State Estimation",
[4] K. A. Greyson and A. Oonsivilai, 2008, WSEAS Journal
[5] K. A. Greyson and A. Oonsivilai, 2008, proceedings ROBIO
[6] Weerakorn Ongsakul and Thawatch Kerdchuen, "Optimal Measurement Placement with Single Measurement Loss Contingency for Power System State Estimation Using Refined Genetic Algorithm," 28th Electrical Engineering Conference (EECON28), Phuket, Thailand.
[7] A. Ketabi and S.A. Hosseini, "A New Method for Optimal Harmonic Meter Placement," American Journal of Applied Sciences 5 (11): 1499- 1505, 2008
[8] A. Oonsivilai and P. Pao-la-or, 2008,"Optimum PID Controller Tuning for AVR System using Adaptive Tabu Search" 12th WSEAS CSCC. Heraklion, Crete Island Greece. July 18-22, 2008
[9] E. Masehian, and M. R. Amin-Naseri, "Sensor-Based Robot Motion Planning - A Tabu Search Approach", IEEE Robotics & Automation Magazine, June 2008, Volume: 15, Issue: 2, pp. 48-57
[10] A. Abur, and A. G. Exposito, Power System State Estimation: Theory and Implementation, Marcel Dekker, Inc. 2004
[11] A. Kumar, B. Das and J. Sharma "Genetic algorithm-based meter placement for static estimation of harmonic sources," IEEE Trans. Power Del., vol. 20, pp. 1088, Apr. 2005.
[12] C. Madtharad , S. Premrudeepreechacharn , N. R. Watson and R. Saeng- Udom "An optimal measurement placement method for power system harmonic state estimation," IEEE Trans. Power Del., vol. 20, pp. 1514, Apr. 2005.
[13] A. Oonsivilai and B. Marungsri, 2008,"Stability Enhancement for Multi- Machine Power System by Optimal PID Tuning of Power System Stabilizer Using Particle Swarm Optimization" WSEAS Transactions on Power System, 2008
[14] A. Oonsivilai and R. Oonsivilai, 2008,"Parameter Estimation of Frequency Response Twin-Screw Food Extrusion Process Using Genetic Algorithms" WSEAS Transactions on Power System, 2008
[15] M. Shahidehpour and Y. Wang, Communication and Control in Electric Power Systems: Applications of Parallel and Distributed Processing, John Wiley & Sons, Inc. 2003.
[16] A. Monticelli, State Estimation in Electric Power Systems: A Generalized Approach, Kluwer Academic Publishers, Massachusetts, 1999.
[17] G. T. Heydt "Identification of harmonic sources by a state estimation technique," IEEE Trans. Power Del., vol. 4, pp. 569, Jan. 1989.
[18] M. P. Young, H. M. Young, B. C. Jin and W.K. Tae, "Design of Reliable Measurement System for State Estimation," IEEE Trans. Power System, vol. 3(3), pp. 830-836, 1988.
[19] P. Zarco, and A. G. Exposito, "Power System Parameter Estimation: a Survey," IEEE Transactions on power Systems, Vol. 15, No. 1, pp. 216- 222, Feb. 2000.
[20] S. A. Zonouz, and W. H. Sanders, "A Kalman-based Coordination for Hierarchical State Estimation: Algorithm and Analysis", Proc. of 41st Hawaii International Conference on System Sciences, 2008.
[21] J. L. Crassidis and J. L. Junkins, Optimal Estimation of Dynamic Systems, CRC Press LLC.
[22] T. Murata, "Petri Nets: Properties, Analysis and Applications", Proc. of the IEEE, Vol. 77. No. 4, April 1989, pp. 541-580.
[23] T. Biswas, A. Davari, A. Feliachi, Modeling and Analysis of Discrete Event Behaviors in Power System using Petri Nets" IEEE 2004.