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Data-Driven Algorithm-Based Blade Icing Fault Prediction for Wind Turbine
编号：34 访问权限：仅限参会人 更新：2025-11-10 11:04:39 浏览：26次 口头报告

报告开始：2025年11月22日 16:20（Asia/Shanghai）

报告时间：10min

所在会场：[S3] Parallel Session 3 [S3-1] Parallel Session 3-22 PM

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摘要
Wind turbine blade icing seriously threatens power generation efficiency and operational safety in cold regions. To address the problem of insufficient accuracy and interpretability in existing icing detection methods, this study proposes a data-driven blade icing fault prediction approach based on Supervisory Control and Data Acquisition (SCADA) operational data. The Synthetic Minority Oversampling Technique (SMOTE) is applied to alleviate class imbalance in icing samples, while Recursive Feature Elimination (RFE) is employed for feature dimension reduction and optimization. Based on the processed dataset, multiple machine learning algorithms—K-Nearest Neighbors (KNN), Random Forest (RF), Stochastic Gradient Descent (SGD), and XGBoost—are systematically evaluated. Results show that the XGBoost model achieves the best prediction performance, with an accuracy of 99.80% and a recall of 99.41%, effectively identifying icing events. To enhance model interpretability, the SHAP method is introduced to analyze the contribution of key features, revealing that wind speed, temperature, and humidity are the most influential factors. This study provides a reliable and interpretable data-driven framework for wind turbine blade icing fault prediction, offering technical support for intelligent operation and maintenance.
关键词
wind turbine blade icing; feature engineering; data-driven algorithms; SHAP; imbalanced data
报告人
Liuxu Wang
PhD graduate student Xi’an Jiaotong University

稿件作者
Liuxu Wang Xi’an Jiaotong University
Wei Deng Xi’an Thermal Power Research Institute Co. Ltd
Fang Wan china；Huaneng Gansu Energy Development Co., Ltd
Min Zhang Northwest University；china
shouwang zhao Xi’an Jiaotong University；College of Electrical Engineering Xi’an Jiaotong University
Ruolan Hu china；Xi’an Thermal Power Research Institute Co. Ltd y Xi’an, China
Gangli Fang Xi’an Thermal Power Research Institute Co. Ltd；china
Yong Zhao Xi’an Thermal Power Research Institute Co. Ltd
Yu Chen Xi'an Jiaotong University
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重要日期

  • 会议日期

    11月21日

    2025

    至

    11月23日

    2025

  • 10月20日 2025

    初稿截稿日期

  • 11月23日 2025

    注册截止日期

主办单位
IEEE Instrumentation and Measurement Society
South China University of Technology
承办单位
South China University of Technology
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