电力系统自动化2026，Vol.50Issue(3)：180-188,9.DOI:10.7500/AEPS20241003001
基于气象数据和深度学习的风机叶片覆冰监测方法
Icing Monitoring Method for Wind Turbine Blades Based on Meteorological Data and Deep Learning
摘要
Abstract
Icing on wind turbine blades is one of the factors that impair the operational conditions of wind turbines and the stability of the power grid.Traditional icing monitoring methods are costly and may potentially damage the original mechanical structure of the blades.This paper establishes an icing monitoring model based on meteorological data and deep learning.By analyzing the Makkonen model from a thermodynamic mechanism perspective,and addressing the limitations of traditional monitoring models in characterizing core parameters such as liquid water content that directly affect icing rate,the model fully considers feature quantities in meteorological data that are closely related to the icing intensity.Meanwhile,time series analysis methods are introduced to capture temporal variation patterns of variables.To tackle the distribution shift problem across wind farm data,a deep adaptive normalization module is designed to perform a domain-invariant transformation on input features.A dual-channel transformer-temporal convolutional network(TCN)architecture is constructed to simultaneously capture global temporal dependencies and local abrupt features of meteorological parameters.Finally,simulations are conducted using actual wind turbine data from a mountainous region.The results show that the model excellently performs in diagnosing icing conditions on wind turbine blades,thereby expanding available technical means for wind turbine blade icing monitoring.关键词
风机/叶片/覆冰/气象数据/时间序列分析/时序卷积网络(TCN)/特征提取/深度学习Key words
wind turbine/blade/icing/meteorological data/time series analysis/temporal convolutional network(TCN)/feature extraction/deep learning引用本文复制引用
李彬,袁军,苏盛,蒙文川,杨再敏..基于气象数据和深度学习的风机叶片覆冰监测方法[J].电力系统自动化,2026,50(3):180-188,9.基金项目
国家自然科学基金资助项目(51777081) （51777081）
广西电网公司科技项目(046000KK52220007). This work is supported by National Natural Science Foundation of China(No.51777081)and Guangxi Power Grid Corporation(No.046000KK52220007). （046000KK52220007）
