(855) 4-ESSAYS

Type a new keyword(s) and press Enter to search

Particle Swarm Optimization

             In this paper, a two-phase hybrid particle swarm optimization (PSO) approach is used to solve optimal reactive power dispatch (ORPD) problem. In this hybrid approach, PSO is used to explore the optimal region and direct search is used as local optimization technique for finer convergence. The performance of the proposed hybrid approach is demonstrated with the IEEE 30-bus and IEEE 57-bus systems and also the performance of this hybrid PSO is compared with that of PSO, Evolutionary Programming (EP) and hybrid EP. The performance of the proposed method is compared with the previous approaches reported in the literature. The performance of hybrid PSO seems to be better in terms of solution quality and computational time.
             Particle swarm optimization, evolutionary programming, direct search, optimal reactive power dispatch, hybrid approach.
             The Optimal Reactive Power Dispatch problem is a non-linear optimization problem with many uncertainties. The loads acquire reactive power for magnetizing purposes at no load conditions. The electric power loads vary from hour to hour. The change of load causes variation in the reactive power requirement. The reactive power will depend on voltage, so that the variation of load causes the variation of voltage. Hence the important operating task is to maintain the voltage within the allowable range for high quality consumer service. The objective of the ORPD is to minimize the system real power loss. This objective can be achieved by employing the various reactive compensation devices such as automatic voltage regulators (AVRs), tap changing transformers and shunt capacitors/reactors [1].
             A wide variety of conventional optimization techniques such as linear programming, Newton approach, interior point methods and dynamic programming [2-6] have been developed to solve ORPD problem. Generally these techniques suffer due to algorithmic complexity, insecure convergence, and sensitivity to initial search point.

Essays Related to Particle Swarm Optimization

Got a writing question? Ask our professional writer!
Submit My Question