A Novel Fault Recovery Strategy for Future Distribution Network based on Multi-objective Particle Swarm Optimization Algorithm

Abstract

Fault recovery strategy plays a vital role in increasing the power system reliability and stability. The classic multi-objective evolutionary algorithm based on Pareto dominance criteria and crowding distance sorting method does not consider the preference of decision maker in the iterative process, which leads to the decline of convergence performance. For the problem, this paper proposes a novel fault recovery strategy based on the preference multi-objective particle swarm algorithm considering the reference vector. This method uses the reference vector to determine the preference area so as to effectively integrate the decision maker’s preference knowledge into the fault recovery plan design. As the multi-objective intelligence algorithm based on Pareto dominance does not consider the problem of decision-makers’ preference knowledge, the multi-objective discrete binary particle swarm algorithm is then introduced. Secondly, the individual solutions are selected through the v-dominance relationship according to the preferences of decision makers, and external files are maintained. Finally, the feasibility of the proposed method is verified through the 69-node distribution network.