Review of Data-Driven Approaches for Wind Turbine Blade Icing Detection

Chang Cai, Jicai Guo, Xiaowen Song (), Yanfeng Zhang, Jianxin Wu, Shufeng Tang, Yan Jia, Zhitai Xing and Qing’an Li ()
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Chang Cai: Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China
Jicai Guo: College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
Xiaowen Song: College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
Yanfeng Zhang: College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
Jianxin Wu: College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
Shufeng Tang: College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
Yan Jia: College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
Zhitai Xing: College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
Qing’an Li: Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China

Sustainability, 2023, vol. 15, issue 2, 1-20

Abstract: Onshore wind turbines are primarily installed in high-altitude areas with good wind energy resources. However, in winter, the blades are easy to ice, which will seriously impact their aerodynamic performance, as well as the power and service life of the wind turbine. Therefore, it is of great practical significance to predict wind turbine blade icing in advance and take measures to eliminate the adverse effects of icing. Along these lines, three approaches to supervisory control and data acquisition (SCADA) data feature selection were summarized in this work. The problems of imbalance between positive and negative sample datasets, the underutilization of SCADA data time series information, the scarcity of high-quality labeled data, and weak model generalization capabilities faced by data-driven approaches in wind turbine blade icing detection, were reviewed. Finally, some future trends in data-driven approaches were discussed. Our work provides guidance for the use of technical means in the actual detection of wind turbine blades. In addition, it also gives some insights to the further research of fault diagnosis technology.

Keywords: wind turbine; SCADA; data-driven approaches; blade icing detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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