Multivariate Time-Series Modelling for Wind Turbine Subsystem Reliability Prediction

Abstract Wind turbine reliability monitoring and prediction are crucial for early failure detection, proactive maintenance, performance optimization, and cost reduction, especially as many utility-scale turbines near the midpoint or end of their operational lifespan. We proposed a wind turbine subsystem reliability prediction model to facilitate week-ahead forecasts of the occurrence, duration, and type of potential failures or probability of downtime for major turbine components, including the blade, hub, gearbox, generator, inverter, electrical, and control subsystems. Specifically, we developed an Instance-Normalization Decomposition Linear (IN-DLinear) model, grounded in deep time-series modelling theory. The distribution shifts in turbine state features across the training, validation, and test datasets, as well as across various time scales, were effectively mitigated with IN. The long-term inertia in turbine state features was addressed by decomposing the input time-series data to effectively capture seasonality. The efficacy of IN-DLinear is systematically evaluated using 10-year field measurements from a 2.5-MW wind turbine. IN-DLinear exhibited superior performance, reducing prediction errors by 13%∼30% compared to mean value judgment and other deep time-series models, including Seq2Seq, Transformer, and Autoformer.