Dynamic Rating Optimization of Transmission Lines Using Particle Swarm Algorithm and Meteorological Data

With the construction of new power systems, the development of advanced power grids that are distributed power integration, facilitate bidirectional interaction between sources, grids, and loads, and are intelligent and efficient has become a major trend for the future development of power grids in China. This paper takes the $\mathbf{2 2 0 k V}$ transmission line Rehe A and B lines in Changchun city as the research object. Based on historical meteorological data and temperature data from weak sections, the thermal balance equation is employed, and a particle swarm optimization algorithm is utilized to introduce error polynomials of meteorological data for algorithm optimization, thereby establishing a physical model for the dynamic rating of specific lines. By calculating the mean absolute error and root mean square error of different error polynomials, the results indicate that the established physical model for dynamic rating calculation has higher accuracy.