A hybrid evolutionary algorithm for secure multi-objective distribution feeder reconfiguration

Distribution Feeder Reconfiguration (DFR) is an important technique to improve the performance of distribution networks. The common objectives considered in the DFR problem are power loss and voltage deviation which are important objectives for traditional distribution systems. Security issues cause by Distributed Generations (DGs) in modern distribution systems which can potentially jeopardize power system security has almost neglected in power system operation problem. Toward this end, this study considers the power loss, Voltage Stability Index (VSI), and number of switching as objective functions which can satisfy both operation and security expectations. The Backward-Forward Sweep (BFS) method known for easy convergence has been employed for power flow calculations. Because of the increase in DG penetration in distributed systems, the impacts of these units are investigated. A powerful optimization algorithm based on hybridization of Shuffled Frog Leaping Algorithm (SFLA) and Particle Swarm Optimization (PSO) is proposed to solve the proposed problem. The proposed algorithm is a combination of strong mutation operator, original SFLA and original PSO algorithms which has high population diversity and search ability. The proposed algorithm has been applied to a complex multimodal benchmark function and also two different distribution networks including 33- and 95-bus test systems.