Multi-Dimensional Analysis and Variability Modeling of Meteorological Data in Microtopographic Regions of Power Grids

The meteorological characteristics of complex terrain areas show significant local variability, which constitutes a potential threat to power grid security. Traditional meteorological models are limited by the lack of terrain parameterization accuracy, which makes it difficult to accurately portray the spatial and temporal variability of micro-scale meteorological elements. In this study, we focus on the microtopographic region of power grid engineering, construct a feature extraction framework that integrates heterogeneous meteorological data from multiple sources, and reveal the coupling mechanism between meteorological elements and terrain parameters. Through spatial and temporal feature correlation modeling and multidimensional information fusion, the dimensional limitations of traditional weather prediction are broken through. The developed micrometeorological prediction model integrates multiple regression algorithms and dynamic optimization strategies to establish a prediction system for key parameters such as temperature, humidity, wind speed, etc., which provides refined meteorological support for the design of power grid disaster prevention and enhances the resilience of the power system under extreme weather conditions.