Multi-objective allocation of measuring system based on binary particle swarm optimization

Abstract

Due to the size and complexity of power network and the cost of monitoring and telecommunication equipment, it is unfeasible to monitor the whole system variables. All system analyzers use voltages and currents of the network. Thus, monitoring scheme plays a main role in system analysis, control, and protection. To monitor the whole system using distributed measurements, strategic placement of them is needed. This paper improves a topological circuit observation method to minimize essential monitors. Besides the observability under normal condition of power networks, the observability of abnormal network is considered. Consequently, a high level of system reliability is carried out. In terms of reliability constraint, identification of bad measurement data in a given measurement system by making theme sure to be detectable is well done. Furthermore, it is maintained by a certain level of reliability against the single-line outages. Thus, observability is satisfied if all possible single line outages are plausible. Consideration of these limitations clears the role of utilizing an optimization algorithm. Hence, particle swarm optimization (PSO) is used to minimize monitoring cost and removing unobservable states under abnormal condition, simultaneously. The algorithm is tested in IEEE 14 and 30-bus test systems and Iranian (Mazandaran) Regional Electric Company.

Keywords

optimal allocation / phasor measurement units / observability / binary particle swarm optimization

References

[1] Bollen M H J. Understanding Power Quality previous Problems: Voltage Sags and Interruptions. Wiley-IEEE Press, 2000

[2] Ahmadi Y, Alinejad-Beromi Y, Moradi M. Optimal PMU placement for power system observability using binary particle swarm optimization and considering measurement redundancy. Expert Systems with Applications, 2011, 38(6): 7263-7269

[3] Sodhi R, Srivastava S C, Singh S N. Optimal PMU placement method for complete topological and numerical observability of power system. Electric Power Systems Research, 2010, 80(9): 1154-1159

[4] Chakrabarti S, Kyriakides E. Optimal placement of phasor measurement units for power system observability. IEEE Transactions on Power Systems, 2008, 23(3): 1433-1440

[5] Won D J, Moon S I. Optimal number and locations of power quality monitors considering system topology. IEEE Transactions on Power Delivery, 2008, 23(1): 288-295

[6] Abur A, Gómez A E. Power System State Estimation: Theory and Implementation. New York: Marcel Dekker, 2004

[7] Rakpenthai C, Premrudeepreechacharn S, Uatrongjitb S, Watson N R. Measurement placement for power system state estimation using decomposition technique. Electric Power Systems Research, 2005, 75(1): 41-49

[8] Phadke A G, Thorp J S. Synchronized Phasor Measurements and Their Application. New York: Springer, 2008

[9] Korres G N, Manousakis N M. State estimation and bad data processing for systems including PMU and SCADA measurements. Electric Power Systems Research, 2011, 81(7): 1514-1524

[10] Jabr R A. Power system state estimation using an iteratively reweighted least squares method for sequential L₁-regression. International Journal of Electrical Power & Energy Systems, 2006, 28(2): 86-92

[11] Eldery M A, El-Saadany E F, Salama M M A, Vannelli A. A novel power quality monitoring allocation algorithm. IEEE Transactions on Power Delivery, 2006, 21(2): 768-777

[12] Chen J, Abur A. Placement of PMUs to enable bad data detection in state estimation. IEEE Transactions on Power Systems, 2006, 21(4): 1608-1615

[13] Xu B, Abur A. Observability analysis and measurement placement for systems with PMUs. In: Proceedings of IEEE Power System Conference and Exposition. 2004, 2: 943-946

[14] Yang X, Zhang X P, Zhou S Y. Coordinated algorithms for distributed state estimation with synchronized phasor measurements. Applied Energy, 2012, 96: 253-260

[15] Milosevic B, Begovic M. Non-dominated sorting genetic algorithm for optimal phasor measurement placement. IEEE Transactions on Power Systems, 2003, 18(1): 69-75

[16] Kennedy J, Eberhart R C. A discrete binary version of the particle swarm algorithm. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. 1997, 5: 4104-4109

[17] Gomez M, Lopez A, Jurado F. Optimal placement and sizing from standpoint of the investor of photovoltaics grid-connected systems using binary particle swarm optimization. Applied Energy, 2010, 87(6): 1911-1918

[18] Engelbrecht A P. Computational Intelligence: An Introduction. John Wiley and Sons, 2007, ch. 16: 289-358

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