Intelligent Optimization of Line Parameters Based on the Inverse Problem of Spatial Electric Field Strength for Ultra-High Voltage AC Transmission Lines

This article proposes a method based on intelligent optimization calculation of line parameters for the inverse problem of spatial electric field strength of ultra-high voltage AC transmission lines. Traditional transmission line design methods mainly rely on experience and trial and error, which have problems of low efficiency and unstable design results. To solve this problem, this paper combines genetic algorithm and finite element method to establish the mapping relationship between line parameters and spatial electric field strength, and proposes an optimization algorithm based on genetic algorithm. Through iterative calculation and fitness evaluation, the optimal combination of line parameters is found. Based on the geometric shape of the transmission line and the electrical characteristics of the wires, the finite element method is used to numerically simulate the electric field distribution of the transmission line, and the spatial electric field strength is optimized as the objective function. By using mathematical formulas and numerical calculations, a mapping relationship between line parameters and spatial electric field strength has been established, providing a foundation for subsequent optimization calculations. By simulating natural selection, genetic variation, and gene crossover, it seeks the optimal solution to a problem. In this article, genetic algorithm is applied to the optimization calculation of line parameters, gradually optimizing the values of line parameters through steps such as population initialization, fitness calculation, selection operation, crossover operation, and mutation operation, with the goal of minimizing the spatial electric field strength [1]. The experimental results show that the method proposed in this article can effectively reduce the spatial electric field strength of the transmission line, improve the safety and stability of the transmission line. Meanwhile, this article also analyzed the convergence and stability of the algorithm, verifying its feasibility and effectiveness [2].