Skip to Main Content
Network reconfiguration is an important tool to optimize the operating conditions of an electrical distribution system. This is accomplished modifying the network structure of distribution feeders by changing the open/close status of sectionalizing switches. This not only reduces the power losses, but also relieves the overloading of the network components. Network reconfiguration belongs to a complex family of problems because of their combinatorial nature and multiple constraints. This paper proposes a solution to this problem, using a Real Coded Quantum Inspired Evolutionary Algorithm (RCQIEA), with a novel codification. In this paper, RCQIEA is tested and compared to an Exhaustive Algorithm (EA), a Heuristic algorithm (HA) and a Genetic Algorithm (GA) on three test systems. Simulation results show that RCQIEA performs better than EA, HA and GA in terms of speed and accuracy. RCQIEA is a highly scalable algorithm as well.