重庆大学学报2026，Vol.49Issue(3)：1-12,12.DOI:10.11835/j.issn.1000-582X.03.001
风力发电机覆冰在线监测动态预警模型
A dynamic early warning model for online monitoring of wind turbine blade icing
摘要
Abstract
Blade icing frequently occurs on wind turbines operating in cold weather conditions,leading to reduced power output,unstable equipment operation,and even severe mechanical failures.Therefore,developing effective early warning methods for wind turbine icing is of great practical significance.In this study,Supervisory Control and Data Acquisition(SCADA)operational data are analyzed,and key features are constructed based on wind speed,power output,and ambient temperature.An early warning model for blade icing events is established using a random forest algorithm.In addition,real-time monitoring of ice thickness is achieved through a rotating cylindrical array device,based on which a real-time icing early warning model and a dynamic warning mechanism are developed.A 3.2 MW wind turbine at the Wanbao Wind Farm in Chongqing is used as a case study to validate the proposed approach.The results show that the icing occurrence warning model achieves a classification accuracy exceeding 95%,and warning signals are issued multiple times within 1 h prior to blade icing events.Furthermore,the real-time warning model continues to generate alerts after icing occurs,demonstrating its capability to continuously track the evolution of the turbine icing environment.Overall,the proposed dynamic early warning model provides effective decision support for the safe operation and efficient management of wind turbines.关键词
风力发电机/覆冰在线监测/旋转圆柱阵列/动态预警Key words
wind turbine/blade icing online monitoring/rotating cylindrical array/dynamic early warning分类
信息技术与安全科学引用本文复制引用
胡琴,饶立鹏,王力,蒋兴良,舒立春..风力发电机覆冰在线监测动态预警模型[J].重庆大学学报,2026,49(3):1-12,12.基金项目
重庆市科技局资助项目(cstc2021jscx-dxwtB0002).Supported by Chongqing Science and Technology Bureau(cstc2021jscx-dxwtB0002). （cstc2021jscx-dxwtB0002）
