Numerical Simulation of Hybrid GSA Based Optimal Power Flow for Multi Objective Optimization Strategy

Abstract

Restricted nonlinear optimization in electric power systems engineering is a topic of Optimal Power Flow (OPF) that has been extensively investigated. It has been a long and remarkable history for the OPF, which was founded in the 1960s, of research and publication. Newcomers to OPF research face a challenging undertaking since there is so much information available and because OPF's popularity within the electric power systems community has prompted authors to presume a considerable deal of prior knowledge that readers unfamiliar with electric power systems may not have.. A significant area of study in the field of productive power framework control and arrangement is the execution and dependability of opf algorithms. In order to achieve a specific aim, Ideal Power Flow is directed. Specific job or multiple capacities might be specified as the aim for this capacity. When it comes to reducing the expense of fuel, we have implemented an optimum power stream in order to keep voltage and power output of the generator within the recommended point-of-limitation. Depending on the benefits and requirements, a different target may be used. Various scholars for the OPF problem have consolidated many streamlined system models, such as linear programming, non-linear programming, quadratic programming, Newton-based techniques, parametric methods, and interior point methods, in the past. Soft computing processes are now being considered for use in place of standard algorithms because of the problems they cause. In order to overcome these drawbacks, it becomes essential to develop soft computing-based optimization algorithms. There are numerous cutting-edge optimization methods like Evolutionary Programming, Genetic Algorithms, PSO Algorithm, etc., that have been proposed in writing to address the issue of over fitting functions (OPF). A particle swarm optimization algorithm has been enhanced in this proposal to reduce cost capacity while keeping imperatives within acceptable limits. The hybridization of particle swarm and gravitational search algorithms is used to make changes to particle swarm optimization. IEEE-118 bus framework is used in the suggested technique. As compared to current methods, the results demonstrate that the algorithm we developed performs better..