Affiliations: Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran | Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
Abstract: The proposed approach presents a hybrid evolutionary algorithm to overcome the Distribution Network Reconfiguration (DNR) problem. This approach combines the Chaotic Particle Swarm Optimization (CPSO) and Teaching-Learning-Based Optimization (TLBO) to find the global optima in more efficient way. Similar to the other evolutionary algorithms, in order to achieve a proper performance, PSO has some parameters i.e. inertia weight factor, which should be adjusted. But the TLBO is free from adjusting parameters and find the optimum solution without tuning any parameters. In order to overcome the problem of the tuning of the PSO algorithms a chaotic framework is implemented to tune the inertia weight factor dynamically. The learning factors of PSO algorithm are considered as a fix number. The CPSO is mixed to TLBO in order to improve the quality of solutions. The obtained hybrid algorithm is applied to minimize the electrical power loss of distribution network by usage of the network reconfiguration. To validate the effectiveness of the proposed algorithm it is applied to two test systems.
Keywords: Distribution network reconfiguration (DNR), chaotic particle swarm optimization (CPSO), teaching-learning-based optimization (TLBO), power loss and evolutionary optimization algorithm
