Wind Turbine Blade Icing Predictive Fault Warning System Based on Conditional Time Series Denoising Diffusion Data Restoration Model

Existing warning methods are hindered by the ability to extract subtle early-stage blade icing features from wind turbine (WT) monitoring data, resulting in delayed or unreliable alerts. To address this challenge, this paper proposes a novel diffusion-based normal WT behavior model, Conditional Time Series Denoising Diffusion (CTSDD), and develops an automated predictive fault warning system accordingly. CTSDD restores normal monitoring data from noise, which encodes conditional information generated by a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hybrid Masking</i> strategy to guide the restoration process. Its conditional encoding and noise prediction networks adopt a Transformer-like symmetric structure, integrating Dual-Attention and bidirectional long short-term memory (BiLSTM) modules to enhance feature extraction for multidimensional time series data. During the early stages of blade icing, deviations from learned normal patterns result in a noticeable decline in wind power (WP) restoration accuracy. Leveraging these deviations, this study proposes a composite scoring algorithm and a thresholding scheme to evaluate WT status and enable timely early warnings. Comparative experiments validate CTSDD’s superior restoration performance, with its system triggering the earliest and most stable warnings—approximately five hours before fault escalation. Ablation studies further confirm the contributions of Dual-Attention and BiLSTM modules. The proposed system offers an intelligent and effective solution to enhance the safety and reliability of WT operations.