集成随机配置网络在输电线路覆冰预测中的应用  

Application of ensemble stochastic configuration network in prediction model of power transmission line icing

作　　者：原辉 胡帆[2] 范晶晶 俞华 王帅 YUAN Hui;HU Fan;FAN Jingjing;YU Hua;WANG Shuai(State Grid Shanxi Electric Power Research Institute,Taiyuan 030001,China;State Grid Shanxi Electric Power Company,Taiyuan 030021,China)

机构地区：[1]国网山西省电力公司电力科学研究院,山西太原030001 [2]国网山西省电力公司,山西太原030021

出　　处：《测绘通报》2025年第1期29-34,共6页Bulletin of Surveying and Mapping

基　　金：国网山西省电力公司科技项目(52053023000D)。

摘　　要：对输电线路进行覆冰预测是保障电网安全运行的关键技术。由于需要综合考虑地形和气象变化等的影响,覆冰预测是一项具有高维非线性、多模态异质性的复杂任务。本文提出了一种基于集成随机配置网络的深度学习方法预测输电线路覆冰。首先根据多尺度融合的小波模极大值进行覆冰图像数据边缘检测,提高覆冰线路识别的准确率;然后考虑历史观测数据中的微地理和微气象等特征,通过多种特征要素组合构建Boosting集成学习框架下随机配置网络预测模型,预测输电线路覆冰情况。算例分析结果表明,本文提出的集成模型优于单一模型,可以有效实现覆冰输电线路识别和厚度预测,提高了模型泛化能力和覆冰灾害预测精度。The prediction of icing on power transmission lines is a key technology to ensure the safe operation of the power grid.Icing prediction is a complex task characterized by high-dimensional nonlinearity and multimodal heterogeneity,as it necessitates the comprehensive consideration of terrain and meteorological changes.This paper proposes a deep learning approach based on an ensemble random configuration network to predict icing on transmission lines.Icing transmission line recognition is enhanced by utilizing multiscale fusion of wavelet mod-maxima for icing image edge detection.Considering features such as micro-geography and micro-meteorology in historical observational data,a Boosting ensemble learning framework is employed along with a random configuration network prediction model to forecast icing conditions on transmission lines.Case study analysis demonstrates that the proposed ensemble model outperforms individual models,effectively achieving icing transmission line recognition and thickness prediction,thereby enhancing model generalization capability and improving the accuracy of icing disaster prediction.

关 键 词：覆冰预测 随机配置网络 集成学习 预测模型 多尺度融合 

分 类 号：P208[天文地球—地图制图学与地理信息工程]