PSO BASED PSS DESIGN FOR TRANSIENT STABILITY ENHANCEMENT

Abstract

In this paper, optimal tuning the parameters of a power system stabilizer (PSS) controller for the power system transient stability enhancement is introduced. The design problem of the proposed PSS is converted to an optimization problem with the time-domain based objective function which is solved by using particle swarm optimization (PSO) technique with a robust ability in order to find the most promising results. The dynamic performance PSS controller is evaluated on the basis of a multi-machine power system exposed to the diverse disturbances by comparison with the genetic algorithm-based damping controller. By virtue of the nonlinear time-domain simulation and some performance indices studies, the performance of the proposed PSS controller is tested and observed.   The results show that the tuned PSO based PSS damping controller by the proposed fitness function has an excellent capability in damping power system low frequency oscillations, as well as it significantly improves the dynamic stability of the power systems. In addition, the results reveal that the performance of the designed controller is better than the genetic algorithm based stabilizer.

Keywords

• Multi-machine power system
• power system stabilizer (PSS)
• transient stability
• particle swarm optimization (PSO)
• genetic algorithm (GA)

References

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27. Data for the studied three-machine nine-bus power
28. system. All data are in pu unless specified otherwise.
29. For further information, see Ref. [19]. H1 23.64, H 2 6.4, H 3 3.01, D 1 0, D2 D3 1dx x1qq x2qq x3qq 0.0969, 0.8645, 1.2578, 0.0608, x1dd 0.1198, x2dd 0.1813, x3dd 8.96, do do1 T1 Exciter: KA 1A 5.89 T3 2A 100, T 3A KA KA 1A 0.05 T2A T3A APPENDIX B
30. Based on the mechanism of the natural selection
31. and survival of the fittest, genetic algorithms are
32. considered as stochastic search methods [25].
33. Moreover, they integrate function evaluation with
34. randomized and/or well-structured exchange of
35. information amongst the solutions in order to achieve
36. the global optimum point. The architecture of the GA
37. implementation may be divided into following three
38. basic steps: initial population generation, fitness
39. evaluation and genetic operations. The GA control
40. parameters, like population size, mutation probability
41. and crossover probability, are chosen and a first
42. population of the binary strings of the finite length is
43. randomly generated [26]. Given a random initial
44. population GA operates in cycles called generations, as follows [25]:
45. Every population member is assessed by employing a fitness function.
46. The population undergoes reproduction through several
47. iterations. At least one parent is selected stochastically.
48. Still, strings possessing higher fitness values would have
49. higher probability of contributing an offspring.
50. In order to produce offspring, genetic operators, like
51. crossover and mutation, are assigned to parents.
52. The offspring are placed in the population and the procedure is rerun.
53. The time-domain simulation is carried out and the
54. fitness function, as shown in (15), is optimized so as to
55. arrive at the optimal set of controller parameters. While
56. applying GA, parameters' figure must be indicated.
57. Optimization is terminated by the generations' pre-specified
58. figure for the genetic algorithm.