Optimization of Distributed Processors for Power System: Kalman Filters using Petri Net
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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:59930", author = "Anant Oonsivilai and Kenedy A. Greyson", title = "Optimization of Distributed Processors for Power System: Kalman Filters using Petri Net", 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.", volume = "3", number = "5", pages = "1196-5", }
