Optimal Power Flow Solutions using Directive Independence Grey Wolf Optimizer

Abstract

This study proposes Directive Independence Grey Wolf Optimizer (DIGWO) developed from Grey Wolf Optimizer (GWO). In GWO, wolfs are adhered to all the leaders for updating their position. There is also no guarantee for wolfs to move pervasively over the search space limiting the wolfs' performance and providing poor prey (solution) to problems. To overcome these issues, DIGWO allows wolfs to select the leaders independently for updating their position. The modified random walk strategies and the modified GWO strategy are represented and applied to wolfs in the exploration and exploitation stages. These strategies are balanced by a random parameter to prevent from premature convergence. Beneficially, wolfs can discover excellent prey over the search space. In this study, IEEE 30 bus test system is employed to oversee the proficiency of DIGWO through the objective functions of the optimal power flow (OPF) problems. As a result, DIGWO is efficient in solving the OPF problems comparing to the other optimization methods.