基于组合深度学习与核密度估计的架空输电线路载流量概率预测OA

The ampacity of overhead transmission lines is closely related to meteorological factors such as surrounding wind speed and ambient temperature.Accurate prediction of ampacity is fundamental for fully utilizing transmission line capacity and enhancing the delivery of renewable energy.In this paper,the Complete Ensemble Empirical Mode Decomposition algorithm is employed to decompose meteorological time series into several locally stationary components.Additionally,the Variational Mode Decomposition algorithm is introduced to further process high-frequency components,reducing data complexity.The XGBoost ensemble learning model is used to forecast various meteorological factors,ensuring prediction accuracy and stability.Kernel density estimation is then applied to model the prediction error distribution of meteorological factors.Based on this,a probability prediction method for overhead transmission line ampacity is proposed by integrating deep learning with kernel density estimation.Case studies demonstrate that,compared to two conventional probability prediction methods for ampacity,the proposed approach reduces conservatism indices by 28.54%and 11.93%,respectively,verifying its effectiveness and advantages.