Damping intra-area and interarea oscillations are critical to optimal power flow and stability in a power system. Power system stabilizers (PSSs) are effective damping devices, as they provide auxiliary control signals to the excitation systems of generators. The proper selection of PSS parameters to accommodate variations in the power system dynamics is important and is a challenging task particularly when several PSSs are involved. Two classical bio-inspired algorithms, which are smallpopulation- based particle swarm optimization (SPPSO) and bacterial foraging algorithm (BFA), are presented in this paper for the simultaneous design of multiple optimal PSSs in two power systems. A classical PSO with a small population of particles is called SPPSO in this paper. The SPPSO uses the regeneration concept, introduced in this paper, to attain the same performance as a PSO algorithm with a large population. Both algorithms use time domain information to obtain the objective function for the determination of the optimal parameters of the PSSs. The effectiveness of the two algorithms is evaluated and compared for damping the system oscillations during small and large disturbances, and their robustness is illustrated using the transient energy analysis. In addition, the computational complexities of the two algorithms are also presented.
T. K. Das et al., "Bio-Inspired Algorithms for the Design of Multiple Optimal Power System Stabilizers: SPPSO and BFA," IEEE Transactions on Industry Applications, vol. 44, no. 5, pp. 1445-1457, Institute of Electrical and Electronics Engineers (IEEE), Sep 2008.
The definitive version is available at https://doi.org/10.1109/TIA.2008.2002171
Electrical and Computer Engineering
National Science Foundation (U.S.)
Keywords and Phrases
Nigerian Power System; Bacterial Foraging; Multimachine Power Systems; Particle Swarm Optimization (PSO); Power System Stabilizers (PSSs); Regeneration Stability; Small Population; Transient Energy (TE) Analysis; Computational complexity
International Standard Serial Number (ISSN)
Article - Journal
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