A Manta Ray Foraging Algorithm Solution for Practical Reactive Power Optimization Problem

Abstract

This paper proposes a solution to the power grid system’s reactive power optimization scheduling problem (RPSP) based on a novel manta ray foraging optimization (MRFO) evolutionary algorithm. By applying the penalty function for the reactive power optimization model, the management of the constraints of the RPSP optimization formula is counting on for calculation. The experimental results of the proposed MRFO scheme are contrasted with other approaches for the IEEE-30 bus system, such as particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization algorithm (MFO), and whale optimization algorithm (WOA). Comparative results show that the MRFO algorithm can generate stable, strong convergence, high reliability effectively, and a feasible figuration needed space in solving optimization problems with reactive power optimization.