Wind Turbine Predictive Analytics Using Environmental Clustering

The patent divides the wind farm into multiple clusters, where each cluster contains wind turbines experiencing similar environmental conditions. Instead of using a single predictive model for all turbines, the system creates separate predictive models for each cluster. This segmentation allows each model to be tailored to specific environmental conditions, improving anomaly detection precision while managing system complexity through organized modular structures.

The patent merges wind turbines into clusters based on their environmental conditions, so that turbines with similar conditions share the same predictive model. This approach achieves the benefit of customized predictions for specific conditions while avoiding the excessive complexity of completely individualized models for each turbine. The merging principle balances reliability improvement with manageable system complexity.

The patent segments the monitoring system into cluster-level groups rather than treating the entire wind farm as a single aggregate. Each cluster represents a manageable group of turbines with similar environmental conditions, allowing operators to monitor clusters individually. This provides turbine-specific precision within a simplified operational framework, as operators deal with clustered data rather than individual turbine complexity.

The patent implements preliminary anomaly detection and condition assessment for each cluster before maintenance is performed. By using predictive models to identify which clusters or individual turbines within clusters are likely to fail or are already degrading, the system enables maintenance to be performed proactively on specific turbines that need it. This preliminary action prevents both unnecessary maintenance on healthy turbines and delayed maintenance on degrading ones, balancing productivity with reliability.