Power system networks are complex systems that are highly nonlinear and non-stationary, and therefore, their performance is difficult to optimize using traditional optimization techniques. This paper presents an enhanced particle swarm optimizer for solving constrained optimization problems for power system applications, in particular, the optimal allocation of multiple STATCOM units. The study focuses on the capability of the algorithm to find feasible solutions in a highly restricted hyperspace. The performance of the enhanced particle swarm optimizer is compared with the classical particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and bacterial foraging algorithm (BFA). Results show that the enhanced PSO is able to find feasible solutions faster and converge to feasible regions more often as compared with other algorithms. Additionally, the enhanced PSO is capable of finding the global optimum without getting trapped in local minima.
Y. del Valle et al., "Enhanced Particle Swarm Optimizer for Power System Applications," Proceedings of the 2008 IEEE Swarm Intelligence Symposium (2008, St. Louis, MO), Institute of Electrical and Electronics Engineers (IEEE), Sep 2008.
The definitive version is available at http://dx.doi.org/10.1109/SIS.2008.4668333
2008 IEEE Swarm Intelligence Symposium (2008: Sep. 21-23, St. Louis, MO)
Electrical and Computer Engineering
Keywords and Phrases
FACTS; Bacterial Foraging Algorithm; Particle Swarm Optimization; Static VAR Compensators; Flexible AC transmission systems; Genetic algorithms
Article - Conference proceedings
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