Node-Depth Encoding with recombination for multi-objective Evolutionary Algorithm to solve loss reduction problem in large-scale distribution systems (original) (raw)

2012

Abstract

ABSTRACT Power loss reduction problem can be achieved by an adequate Distribution System Reconfiguration (DSR). Usually DSR for power loss reduction is formulated as a nonlinear, multi-objective and multi-constrained optimization problem, containing thousands of constraints equations for large-scale networks. As a consequence, to find an adequate solution for such problem is computationally complex. Recently a practical and efficient approach to solve this problem which overcomes such a hurdle was developed. This approach, called MEAN, combines a multi-objective Evolutionary Algorithm (EA), based on subpopulations tables, with a new tree encoding, named Node-Depth Encoding (NDE). This paper presents a new genetic recombination operator, named Evolutionary History Recombination NDE operator, which improves the MEAN performance to solve the loss reduction problem in large-scale networks. Tests with large networks show that with the new operator the MEAN can find network configurations with loss reduction of 29.57% for networks with 5,166 switches. This result is surprising since the practitioners used to expect at most a 10% reduction by means of DSR.

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