Structural damage identification of wind turbine blades based on multi-scale deformable linear Transformer network

Structural damage identification of wind turbine blades (WTBs) is crucial for reducing maintenance costs and ensuring operational reliability. Data-driven methods leveraging supervisory control and data acquisition (SCADA) data have advanced WTBs monitoring, but existing approaches remain limited by models that fail to capture subtle changes in blade damage characteristics. To address these gaps, a novel multi-scale deformable linear Transformer network (MDLTN) is proposed for structural damage identification of WTBs. In contrast to representative temporal Transformer models such as the Temporal Fusion Transformer and Informer, the proposed MDLTN introduces a multi-scale deformable attention mechanism with linear computational complexity, enabling adaptive modeling of scale-varying and damage-sensitive temporal features. Specifically, the proposed network can extract temporal features at various scales by applying a multi-scale time convolution module, and a deformable linear self-attention mechanism is proposed to adaptively capture both local and global temporal dependencies. Due to the limited availability of data across diverse blade regions and varying damage levels, a wind turbine model is established to obtain the blade failure state data, considering the complex wind condition environment based on GH Bladed. The response dataset collects data from different blade sections subjected to various damage degrees under a range of wind speed conditions. The SCADA data are provided by a wind farm, covering 16 distinct measured blade conditions. The analyzing results of two datasets based on the response data from simulated model and the real SCADA data from wind farm demonstrate that the proposed network achieves superior performance in terms of accuracy, robustness, and generalization capability.