Inverse time overcurrent optimization of distribution network with DG based on improved gray wolf algorithm

Abstract

Inverse time overcurrent protection can be applied to distribution networks with distributed power sources because of its superior protection characteristics, but its time limit coordination and value setting are complicated, which limits its large-scale engineering application. Therefore, this paper proposes an inverse time overcurrent protection setting strategy for distribution network(DN) based on improved gray wolf optimizer (GWO) algorithm. Firstly, the current setting optimization model and the protection operation characteristics are established considering the reliability, rapidity and selectivity. Secondly, the GWO algorithm is improved by introducing the population initialization strategy based on elite backward learning, adaptive weights and variation strategy based on the Cauchy operator for the characteristics of the traditional GWO algorithm which is easy to trap in local optimum and low convergence accuracy. The improved algorithm does not introduce new parameters and achieves a balance between global and local. Finally, the case study results represent that the improved GWO algorithm has high accuracy and stability in both two-phase short-circuit and three-phase short-circuit scenarios, and has good practical applicability.