Optimal placement of Distributed Generation using combination of PSO and Clonal Algorithm

The optimal placement of Distributed Generation (DG) has attracted many researchers' attention recently due to its ability to obviate defects caused by improper installation of DG units, such as rise in system losses, decline in power quality, voltage increase at the end of feeders and etc. This paper presents a new advanced method for optimal allocation of DG in distribution systems. In this study, the optimum location of DG units is specified by introducing the power losses and voltage profile as variables into the objective function. Particle Swarm Optimization (PSO) and Clonal Selection Algorithm (CLONALG) are two methods which have been applied to optimize different objective functions in previous studies. In this paper, the Combination of Particle Swarm Optimization and Clonal Selection Algorithm (PCLONALG) is utilized as a solving tool to acquire superior solutions. Considering the fitness values sensitivity in PCLONALG process, it is necessary to apply load flow for decision making. Finally, the feasibility of the proposed technique is demonstrated for a typical distribution network and is compared with the PSO and CLONALG methods. The experimental results illustrate that the PCLONALG method has a higher ability in comparison with PSO and CLONALG, in terms of quality of solutions and number of iterations. The approach method has the preferences of both previous methods. Via immunity operation, the diversity of the antibodies is maintained and; the speed of convergence is ameliorated by operating particle swarm intelligence.