Power system stabilizer design using Strength Pareto multi-objective optimization approach

Abstract Power system stabilizers (PSSs) are the most well-known and effective tools to damp power system oscillation caused by disturbances. To gain a good transient response, the design methodology of the PSS is quite important. The present paper, discusses a new method for PSS design using the multi-objective optimization approach named Strength Pareto approach. Maximizations of the damping factor and the damping ratio of power system modes are taken as the goals or two objective functions, when designing the PSS parameters. The program generates a set of optimal parameters called Pareto set corresponding to each Pareto front, which is a set of optimal results for the objective functions. This provides an excellent negotiation opportunity for the system manager, manufacturer of the PSS and customers to pick out the desired PSS from a set of optimally designed PSSs. The proposed approach is implemented and examined in the system comprising a single machine connected to an infinite bus via a transmission line. This is also done for two familiar multi-machine systems named two-area four-machine system of Kundur and ten-machine 39-bus New England system. Parameters of the Conventional Power System Stabilizer (CPSS) are optimally designed by the proposed approach. Finally, a comparison with famous GAs is given.

[1]  Kwee-Bo Sim,et al.  Solution of multiobjective optimization problems: coevolutionary algorithm based on evolutionary game theory , 2004, Artificial Life and Robotics.

[2]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[3]  M. Pai Energy function analysis for power system stability , 1989 .

[4]  M. A. Abido,et al.  Robust design of multimachine power system stabilisers using tabu search algorithm , 2000 .

[5]  N. Kumaresan,et al.  A Hybrid Genetic Algorithm based Power System Stabilizer , 2007, 2007 International Conference on Intelligent and Advanced Systems.

[6]  P. Kundur,et al.  Power system stability and control , 1994 .

[7]  R. Grondin,et al.  IEEE PSS2B versus PSS4B: the limits of performance of modern power system stabilizers , 2005, IEEE Transactions on Power Systems.

[8]  M. Dubey,et al.  Simultaneous Stabilization of Multimachine Power System Using Genetic Algorithm Based Power System Stabilizers , 2006, Proceedings of the 41st International Universities Power Engineering Conference.

[9]  D.M. Falcao,et al.  Coordinated tuning of AVRs and PSSs by multiobjective genetic algorithms , 2005, Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems.

[10]  G. J. Rogers,et al.  Analytical investigation of factors influencing power system stabilizers performance , 1992 .

[11]  Akhtar Kalam,et al.  A direct adaptive fuzzy power system stabilizer , 1999 .

[12]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[13]  H. Happ Power system control and stability , 1979, Proceedings of the IEEE.

[14]  A. Gharaveis,et al.  Application of CDCARLA Technique in Designing Takagi-Sugeno Fuzzy Logic Power System Stabilizer (PSS) , 2006, 2006 IEEE International Power and Energy Conference.

[15]  M. A. Abido,et al.  Optimal multiobjective design of robust power system stabilizers using genetic algorithms , 2003 .

[16]  H. Singh,et al.  Introduction to game theory and its application in electric power markets , 1999 .

[17]  Y. Sergeyev,et al.  Tuning fuzzy power-system stabilizers in multi-machine systems by global optimization algorithms based on efficient domain partitions , 2008 .

[18]  Narayana Prasad Padhy,et al.  MATLAB/SIMULINK Based Model of Single- Machine Infinite-Bus with TCSC for Stability Studies and Tuning Employing GA , 2007 .

[19]  J. Nanda,et al.  Multi-machine power system stabilizer design by rule based bacteria foraging , 2007 .

[20]  A. H. Coonick,et al.  Coordinated synthesis of PSS parameters in multi-machine power systems using the method of inequalities applied to genetic algorithms , 2000 .

[21]  Amin Khodabakhshian Pole-zero assignment adaptive stabiliser , 2005 .

[22]  O.P. Malik,et al.  Power System Stabilizer Design Using an Online Adaptive Neurofuzzy Controller With Adaptive Input Link Weights , 2008, IEEE Transactions on Energy Conversion.

[23]  O.P. Malik,et al.  Neurofuzzy Power System Stabilizer , 2008, IEEE Transactions on Energy Conversion.

[24]  M. A. Abido,et al.  Robust design of multimachine power system stabilizers using simulated annealing , 2000 .