No abstract

AbstiractThis paper presents an on-line distribution feeder optimal reconfiguration algorithm fir resistive loss reduction. ArtiJicial neural networks (ANN) were used to assure the application feasibility in real-time. The demand variation used during the ANN training is represented by samplings via Monte Carlo Simulation. A consolidated heuristic algorithm is utilized to obtain the demand topologies. An integer formulation 0-1 is used to guarantee the solution optimality from the initial solution supplied by the ANN. It is also presented the application results to a demonstrative test system, indicating to new applications in real systems where topological alteration are required.