Integrating several subpopulation tables with node-depth encoding and strength Pareto for service restoration in large-scale distribution systems.

摘要

Network reconfiguration for service restoration inuddistribution systems is a combinatorial complex optimizationudproblem that usually involves multiple non-linear constraints andudobjectives functions. For large scale distribution systems, no exactudalgorithm has found adequate restoration plans in real-time. Onudthe other hand, the combination of Multi-objective EvolutionaryudAlgorithms (MOEAs) with the Node-Depth Encoding (NDE) hasudbeen able to efficiently generate adequate restoration plans forudrelatively large distribution systems (with thousands of busesudand switches). The method called MEAN-NDS results from theudcombination of NDE with a technique of MOEA based onudsubpopulation tables and the MOEA called NSGA-II. In orderudto obtain a more efficient MOEA to treat service restorationudproblem in large scale distribution systems, this paper proposesuda new method, which results from the combination of MEANNDSudwith the MOEA called SPEA-2. The idea is to improveudthe capacity of MEAN-NDS to explore both the search andudobjective spaces. Simulations results with distribution systemsudranging from 632 to 1,277 switches, have shown that the proposedudmethod found the configurations of lower switching operations,udand explores the space of the objective solutions better than theudMEAN-NDS, approximating better the Pareto-optimal front.