An online distribution feeder optimal reconfiguration algorithm for resistive loss reduction using a multi-layer perceptron

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.

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