Evolutionary optimization techniques for optimal location and parameter settings of TCSC under single line contingency

Flexible AC transmission systems (FACTS) devices are optimally placed in the power system in order to maximize the system security. One of the most effective FACTS devices is the thyristor controlled series capacitor (TCSC) which can smoothly and rapidly change its apparent reactance according to the system requirements. This paper deals with the application of two evolutionary optimization techniques namely, genetic algorithm (GA) and particle swarm optimization (PSO) for finding the optimal location and the optimal parameter settings of TCSC under single line contingency (N-1 contingency). Contingency analysis is performed to detect and rank the severest line faulted contingencies in a power system. To validate the proposed techniques, simulations are performed on an IEEE 6-bus power system and an IEEE 14-bus power system. The obtained results are encouraging, and show that TCSC is one of the most effective series compensation devices that can significantly eliminate or minimize line overloads against single contingencies. Also the results indicate that both GA and PSO techniques can easily and successfully find out the optimal location and the optimal parameter settings of TCSC.

[1]  Enrique Acha,et al.  A thyristor controlled series compensator model for the power flow solution of practical power networks , 2000 .

[2]  Narain G. Hingorani,et al.  Power electronics in electric utilities: role of power electronics in future power systems , 1988, Proc. IEEE.

[3]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[4]  Dejan J. Sobajic,et al.  An artificial intelligence system for power system contingency screening , 1988 .

[5]  A. Abur,et al.  Improving system static security via optimal placement of thyristor controlled series capacitors (TCSC) , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[6]  Narayana Prasad Padhy,et al.  Optimal location and initial parameter settings of multiple TCSCs for reactive power planning using genetic algorithms , 2004, IEEE Power Engineering Society General Meeting, 2004..

[7]  S. Gerbex,et al.  Optimal Location of Multi-Type FACTS Devices in a Power System by Means of Genetic Algorithms , 2001, IEEE Power Engineering Review.

[8]  Claudio R. Fuerte-Esquivel,et al.  A Newton-type algorithm for the control of power flow in electrical power networks , 1997 .

[9]  S.J. Cheng,et al.  Optimal Location and Parameter Setting of TCSC by Both Genetic Algorithm and Particle Swarm Optimization , 2007, 2007 2nd IEEE Conference on Industrial Electronics and Applications.

[10]  P. Venkatesh,et al.  Application of PSO technique for optimal location of FACTS devices considering system loadability and cost of installation , 2005, 2005 International Power Engineering Conference.

[11]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[12]  Y. Besanger,et al.  A multi-objective genetic algorithm approach to optimal allocation of multi-type FACTS devices for power systems security , 2006, 2006 IEEE Power Engineering Society General Meeting.

[13]  S.M.R. Slochanal,et al.  Application of PSO technique to find optimal settings of TCSC for static security enhancement considering installation cost , 2005, 2005 International Power Engineering Conference.

[14]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[15]  L. L. Freris,et al.  Investigation of the load-flow problem , 1967 .

[16]  Philip G. Hill,et al.  Power generation , 1927, Journal of the A.I.E.E..

[17]  N.P. Padhy,et al.  Newton-Raphson TCSC model for power flow solution of practical power networks , 2002, IEEE Power Engineering Society Summer Meeting,.

[18]  David Coley,et al.  Introduction to Genetic Algorithms for Scientists and Engineers , 1999 .

[19]  Xiongxiong He,et al.  Modeling identification of power plant thermal process based on PSO algorithm , 2005, Proceedings of the 2005, American Control Conference, 2005..

[20]  G. Radman,et al.  Contingency selection and static security enhancement in power systems using heuristics-based genetic algorithms , 2004, Thirty-Sixth Southeastern Symposium on System Theory, 2004. Proceedings of the.

[21]  G. C. Ejebe,et al.  Fast contingency screening and evaluation for voltage security analysis , 1988 .

[22]  Pierluigi Siano,et al.  Selection of optimal number and location of thyristor-controlled phase shifters using genetic based algorithms , 2004 .

[23]  Y. Besanger,et al.  Blackout prevention by optimal insertion of FACTS devices in power systems , 2005, 2005 International Conference on Future Power Systems.

[24]  Ali Abur,et al.  Static security enhancement via optimal utilization of thyristor-controlled series capacitors , 2002 .

[25]  O. P. Malik,et al.  Multi-contingency preprocessing for security assessment using physical concepts and CQR with classifications , 1993 .

[26]  M. Damborg,et al.  Towards static-security assessment of a large-scale power system using neural networks , 1992 .