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.

Mycelium-based biocomposites represent a novel class of environmentally friendly materials. This study investigated the potential of using Salix psammophila and peanut straw as substrates for cultivating Pleurotus ostreatus and Ganoderma lucidum, respectively, to fabricate mycelium-based cushioning materials. The results demonstrated that the Pleurotus ostreatus-based cushion material using Salix psammophila (POSM) outperformed the Ganoderma lucidum-based cushion material using peanut straw (GLPM) in terms of overall performance. Both materials presented optimal comprehensive properties when the cultivation period reached 30 days. Increasing the substrate length enhanced most of the material properties. The resulting density ranged from 0.13 to 0.16 g/cm³, which was higher than that of polystyrene foam. The contact angles of both materials exceeded 120°, whereas their elastic springback rates reached 50.2% and 43.2%, and their thermal conductivities were 0.049 W/m·K and 0.051 W/m·K, respectively. Additionally, thermogravimetric analysis revealed that both materials exhibited similar thermal degradation behavior and relatively high thermal stability. These findings align with those of previous studies on mycelium composites and indicate that the physical and mechanical properties of the materials are largely comparable to those of expanded polystyrene (EPS). In conclusion, the developed mycelium-based cushioning materials promote the efficient utilization of agricultural residues and hold promise as a sustainable alternative to EPS, offering broad application prospects in the transportation and packaging sectors.

With the increasing installed capacity of wind turbines, ensuring the safe operation of wind turbines is of great significance. However, the failure of wind turbines is still a severe problem, especially as blade damage can cause serious harm. To detect blade damage in time and prevent the accumulation of microdamage of blades evolving into severe injury, a damage dataset based on GH Bladed simulation of blade damage is proposed. Then, based on the wavelet packet analysis theory method, the MATLAB software can automatically analyze and extract the energy characteristics of the signal to identify the damage. Finally, the GH Bladed simulation software and MATLAB software are combined for fault diagnosis analysis. The results show that the proposed method based on GH Bladed to simulate blade damage and wavelet packet analysis can extract damage characteristics and identify single-unit damage, multiple-unit damage, and different degrees of damage. This method can quickly and effectively judge the damage to wind turbine blades; it provides a basis for further research on wind turbine blade damage fault diagnosis.