Optimal Power Flow Using Improved Grey Wolf Optimization Algorithm

This paper presents an Improved Grey Wolf Optimization (I-GWO) method for resolving the optimal power flow (OPF) issue. The I-GWO algorithm is proposed to improve population diversity, trade-off between exploration and exploitation, and also to avoid early convergence problems. The IGWO employs a dimension learning-based hunting (DLH) search technique acquired from the natural hunting behavior of individual wolves. The DLH strategy enhances the search capability and preserves diversity. The considered objectives are minimizing total fuel cost, active power loss, and voltage variations. A penalty method is used to tackle different constraints of the OPF issue. The I-GWO and GWO algorithms have been examined on the IEEE 30 -bus power system, and the obtained results are compared with other algorithms as available in the literature. The obtained outcomes are contrasted with other algorithms to show the superiority and robustness of the I-GWO method to solve the OPF problem.