Diagnosis of Wind Turbine Blade Mass Imbalance Based on a Lightweight Transformer Model

• Published in: Kongzhi Yu Xinxi Jishu
• Main Authors: LI Jingming, CHENG Shu, XIANG Chaoqun, LIU Hongwen, JIANG Tao, ZHOU Ruirui
• Format: Article
• Language: Chinese
• Published: Editorial Office of Control and Information Technology 2025-02-01
• Subjects: wind turbine; variational mode decomposition(VMD); lightweight; Transformer model; positional encoding
• Online Access: http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2025.01.002

Mass imbalance in wind turbine blades can result in significant centrifugal forces during rotation, leading to mechanical vibrations throughout the turbine system. Prolonged operation under vibration conditions can cause stress concentration in key components such as the blades, main shaft, and the transmission system, ultimately reducing the equipment's lifespan and lowering the power generation efficiency. To address the challenges of signal extraction for vibrations induced by blade imbalance, as well as the low diagnostic accuracy and complex structures of existing models, this paper proposes a lightweight Transformer model based on signal input subjected to variational mode decomposition (VMD) for diagnosing mass imbalance in wind turbine blades. Original vibration signals are decomposed into multi-modal time-domain signals to increase the dimensionality of fault signals. To reduce the model's complexity, an improved Transformer diagnostic model without positional encoding is introduced, thereby simplifying the model structure. An experimental analysis was conducted using a supervisory control and data acquisition (SCADA) system to collect nacelle vibration data. Based on the experimental results, the proposed approach achieved a diagnostic accuracy of up to 98.3% for mass imbalance in wind turbine blades.