Name
Title | ||
|---|---|---|
Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design |
Abstract | ||
|---|---|---|
Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. The GA has been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly non-linear, mixed integer optimization problems that are typical of complex engineering systems. PSO technique is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA optimization techniques, for flexible ac transmission system (FACTS)-based controller design. The design objective is to enhance the power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem and both PSO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques in terms of computational effort, computational time and convergence rate is compared. Further, the optimized controllers are tested on a weakly connected power system subjected to different disturbances over a wide range of loading conditions and parameter variations and their performance is compared with the conventional power system stabilizer (CPSS). The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the techniques in designing a FACTS-based controller, to enhance power system stability. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.asoc.2007.10.009 | Appl. Soft Comput. |
Keywords | Field | DocType |
phillips–heffron model,facts-based controller design,optimization technique,mixed integer optimization problem,genetic algorithm,computational effort,facts-based controller,various modern heuristic optimization,tcsc,particle swarm optimization,power system stability,complex engineering system,optimization problem,facts,ga optimization technique,flexible ac transmission system,objective function,heuristic search,power system,convergence rate | Particle swarm optimization,Mathematical optimization,Control theory,Control theory,Multi-swarm optimization,Engineering optimization,Optimization problem,Genetic algorithm,Mathematics,Flexible AC transmission system,Metaheuristic | Journal |
Volume | Issue | ISSN |
8 | 4 | Applied Soft Computing Journal |
Citations | PageRank | References |
57 | 4.62 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sidhartha Panda | 1 | 211 | 24.23 |
| P. Prasad | 2 | 143 | 15.96 |
| PadhyNarayana Prasad | 3 | 57 | 4.62 |