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. [7].
The expert systems [8], fuzzy logic [9], AI approach [10], fuzzy linear programming [11], evolutionary programming (EP).
[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.