On-Line Approach for Loss Reduction in Electric Power Distribution Networks Using Learning Classifier Systems

Abstract

The problem of minimization of energy losses in power distribution systems can be formulated as obtaining the “best” network configuration, through the manipulation of sectionalizing switches. Using graph terminology, we have a combinatorial optimization problem, whose solution corresponds to finding a minimum spanning tree for the network. As an on-line approach to loss reduction in electric power distribution networks, this paper relies on Learning Classifier Systems to continually proposed network configurations close to the one associated with minimum energy losses, in the case of timevarying profiles of energy requirement. In order to evolve the set of rules that composes the Classifier System, operators for selection, reproduction and mutation are applied. Case studies illustrate the possibilities of this approach.