Innovative hybrid grey wolf-particle swarm optimization for calculating transmission line parameter.

Transmission line (TL) parameters, particularly capacitance, are important for ensuring the efficient and reliable operation of power systems. As power networks become increasingly complex, accurately determining TL parameters faces certain challenges, particularly capacitance, which involves overcoming computational challenges due to bundling configurations. The interconnected nature of modern TL and the use of advanced technologies further add to the complexity. Effective estimation requires advanced measurement techniques and sophisticated computational tools. Recently, optimization techniques have become widely used for TL parameter calculations. However, traditional methods struggle with challenges like limited exploration and slow convergence. To address these issues, this research introduces a hybrid algorithm called HGWPSO, which combines the Grey Wolf Optimizer (GWO) with Particle Swarm Optimization (PSO). The main objective is to enhance the exploitation ability of GWO with the exploration capability of PSO, maximizing the strengths of both variants. Initially, to verify the efficiency of HGWPSO, CEC_19 benchmark functions are utilized to evaluate its performance. Secondly, the main focus of this study is to calculate TL parameters such as capacitance considering two, three, and four-bundle conductors using the HGWPSO algorithm and comparing its performance with other optimization techniques. According to the obtained result, the average percentage reduction for HGWPSO is 0.15 % in test case 1, 4.85 % in test case 2, and 2.84 % in test case 3, compared to others. It shows that the HGWPSO has better performance than other methods regarding convergence speed, and ability to locate the global optimum. Finally, experimental analysis confirms the superiority of the HGWPSO in accurately estimating TL capacitance for different bundle conductor configurations, obtaining lower average values, and effectively addressing the characteristic complexities in the TL parameter.