Hybrid Particle Swarm Optimization Based Optimal Reactive Power Dispatch

Abstract

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. (12), are some of the heuristic techniques that have been used, recently, to solve the ORPD problem. The EP is suitable for solving global optimization problems like ORPD. The only disadvantage of EP is that it takes more computation time (13). This paper proposes a hybrid approach to the optimal reactive power dispatch problem. Particle Swarm Optimization (PSO) is one of the evolutionary computation (EC) technique based on swarm intelligence. It is sensitive to the tuning of its parameters and has a flexible mechanism to explore a global optimum point within a short calculation time (14). By employing the PSO initially the solution quality improves rapidly; later on obtaining the further improvement is very difficult and most of the computation time is spend over obtaining small improvements. To overcome this problem PSO is used for initial exploration and the local search (LS) technique is employed for finer convergence. The convergence of LS techniques depends on the initial search point and quickly finds the local optimum if the starting point is nearer to the optimum (15). This paper employs direct search (DS) (16) as a LS technique. The hybrid approach consists of two phases. In phase-1, PSO is employed to obtain the optimal region quickly and in phase-2, the DS with systematic reduction of the size of the search region (16) is used to find the local optimum. To validate the proposed hybrid method, it is tested on two IEEE standard test systems having non-linear characteristics. The results of the proposed hybrid approach are compared with PSO, EP and hybrid EP. The comparison exhibits the effectiveness of the proposed approach in terms of solution quality and computation time.