Multi-objective Evolutionary Algorithm with Discrete Differential Mutation Operator for Service Restoration in Large-Scale Distribution Systems

Abstract

The network reconfiguration for service restoration in distribution systems is a combinatorial complex optimization problem that usually involves multiple non-linear constraints and objectives functions. For large networks, no exact algorithm has found adequate restoration plans in real-time, on the other hand, Multi-objective Evolutionary Algorithms (MOEA) using the Node-depth enconding (MEAN) is able to efficiently generate adequate restorations plans for relatively large distribution systems. This paper proposes a new approach that results from the combination of MEAN with characteristics from the mutation operator of the Differential Evolution (DE) algorithm. Simulation results have shown that the proposed approach, called MEAN-DE, properly designed to restore a feeder fault in networks with significant different bus sizes: 3,860 and 15,440. In addition, a MOEA using subproblem Decomposition and NDE (MOEA/D-NDE) was investigated. MEAN-DE has shown the best average results in relation to MEAN and MOEA/D-NDE. The metrics \(R_2\), \(R_3\), Hypervolume and \(\epsilon \)-indicators were used to measure the quality of the obtained fronts.