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 Hybrid Masking 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. Note to Practitioners—WT blade icing presents a significant challenge for maintenance, particularly in its early stages, where the subtle impact often goes unnoticed, rendering traditional data analysis ineffective for detection. If left unaddressed, icing progressively degrades WT performance, reducing energy efficiency and increasing operational costs. Therefore, this study introduces an innovative icing predictive warning system based on an advanced deep-learning diffusion model, designed to detect early-stage icing indicators with greater sensitivity, ensuring reliable WT operation and economic efficiency. At its core, the deep learning model is dedicated to learning the typical patterns within normal WT monitoring data. Importantly, it can identify minute deviations in early-stage icing monitoring data from established normal patterns. Leveraging these discrepancies, we develop a WT status scoring algorithm and a calibrated warning threshold, enabling precise and timely early warnings for blade icing. In practice, this system autonomously analyzes monitoring data in real time, promptly issuing accurate icing warnings. This empowers operators to take proactive measures before the fault escalates, enhancing the reliability and intelligence of WT fault management. Future work will explore optimizing data utilization by assessing the value of different monitoring data sources.