AI Based Improved Optimizer for Solving Energy Issues

Abstract

In order to distribute energy over a large area, an overhead power line contains multiple conductors attached to poles. Transmission line parameters are critical to power system stability analysis and state estimation. By taking into account four different aspects of the problem - dimension identification, exploration controls, improved prey encircling and candidate solution choosing - authors have presented an AI based improved grey wolf optimization (IGWO) method for determining the parameters of the overhead AC transmission lines. Improved grey wolf optimization algorithms for calculating capacitance and inductance per unit length were developed for 3-phase with different bundle conductors. Global or nearly global optimal control variable sets are better served by the IGWO methodology than other approaches. Furthermore, the proposed research shows that the or AI based IGWO can compete with existing methods and solve real-world optimization challenges. Because the AI based IGWO algorithms have been enhanced, they are more effective than other methods. It has been statistically proved that the strategy under investigation generated the best possible results for several of these functions. It has been found that the suggested method is computationally faster, more accurate, and more reliable in terms of durability and accuracy.