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

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.