Modified grasshopper optimization framework for optimal power flow solution

Abstract

This paper proposes a modified grasshopper optimization algorithm (MGOA) to solve the optimal power flow (OPF) problem. The conventional GOA is a recent optimization technique that is conceptualized from the natural lifestyle of grasshopper including their movement and migration. The MGOA is based on modifying the mutation process in the conventional GOA in order to avoid trapping into local optima. Different single- and multi-objective functions are solved using the proposed optimization technique. These objective functions consist of quadratic fuel cost minimization, emission cost minimization, active power loss minimization, quadratic fuel cost and active power loss minimization, quadratic fuel cost minimization and voltage profile improvement, quadratic fuel cost minimization and voltage stability improvement, quadratic fuel cost minimization and emission minimization, quadratic fuel cost and power loss minimization, voltage profile and voltage stability improvement. The proposed technique is validated using standard IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus test systems with thirteen case studies. Simulation results reveal the better performance and superiority of the proposed technique to solve various OPF problems compared with well-recognized evolutionary optimization techniques stated in the literature review.