Wind Turbine Peer-Cluster Prognostics for Component Failure Prediction
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Solution Overview
Problem
Wind turbines in farms face challenges in predictive maintenance due to varying environmental conditions and geographic positions, making direct comparison and effective monitoring difficult, leading to increased operational and maintenance costs.
Innovation Solution
A method and system that use environmental and performance sensors to cluster wind turbines based on similar conditions, identify low-performing turbines, and predict the end-of-life of critical components using machine-readable instructions and physics-based models, allowing for targeted maintenance without requiring a comprehensive model for the entire farm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive maintenance techniques are used to monitor and evaluate wind turbine performance, then reliability and availability rate are improved, but the complexity of the system increases due to the need for comprehensive monitoring and comparison models
Solution Approach 1:
The patent segments the wind farm into multiple peer-clusters based on environmental conditions (wind speed, temperature, humidity) and geographic location. Each cluster contains turbines with similar operating characteristics, allowing for localized predictive maintenance models rather than a single comprehensive farm-wide model. This segmentation reduces overall system complexity while maintaining reliability.
Solution Approach 2:
The patent applies local quality by creating peer-clusters with specific environmental and operational characteristics. Each cluster is analyzed and maintained according to its local conditions, allowing maintenance strategies to be tailored to specific turbine groups rather than applying a uniform approach across the entire farm. This improves reliability for each local group while reducing the complexity of managing the entire farm.
2Productivity
If direct comparison of wind turbines is performed to identify performance issues, then maintenance efficiency is improved, but the accuracy decreases due to variations in environmental conditions and geographic position
Solution Approach 1:
The patent divides turbines into peer-clusters based on similar environmental conditions and geographic positions. This segmentation ensures that turbines within each cluster are comparable to one another, improving the accuracy of performance comparisons. By maintaining clusters rather than comparing all turbines directly, the system achieves both efficient maintenance identification and accurate performance measurement.
Solution Approach 2:
The patent changes the parameters used for comparison by normalizing performance metrics according to local environmental conditions (wind speed, temperature, humidity). This allows for accurate comparisons between turbines with different operating conditions by adjusting the comparison parameters to account for environmental variations, thereby improving both accuracy and maintenance efficiency.
3Measurement precision
If comprehensive models for the entire wind farm are developed to predict component failure, then prediction accuracy is improved, but the computational resources and time required increase significantly
Solution Approach 1:
The patent segments the wind farm into peer-clusters and develops predictive models for each cluster rather than creating a single comprehensive farm-wide model. This segmentation reduces the computational burden significantly, as each cluster model processes only the data relevant to its specific turbines and environmental conditions. The time required for model computation is reduced while maintaining high prediction accuracy for each cluster.
Solution Approach 2:
The patent applies partial action by developing and maintaining only the necessary predictive models for specific peer-clusters rather than attempting to model the entire farm simultaneously. This allows the system to achieve high prediction accuracy for the current operational state of each cluster without requiring excessive computational resources or time for the entire farm population.
4Reliability
If environmental conditions are monitored for each wind turbine, then the ability to identify low-performing turbines is improved, but the cost of sensors and monitoring infrastructure increases
Solution Approach 1:
The patent merges environmental monitoring data from multiple turbines within each peer-cluster to create a comprehensive view of local conditions. By combining data from multiple sources (wind speed, temperature, humidity sensors across the cluster), the system achieves reliable performance monitoring without requiring every individual turbine to have its own complete sensor suite. This reduces the overall infrastructure complexity while maintaining monitoring reliability.
Data Source
AI summary
Methods and systems for predicting an end of life of a wind turbine component including receiving environmental conditions indicative of natural surroundings of wind turbines within a wind turbine farm, receiving component performance metrics indicative of an operation of wind turbines within a wind turbine farm, and distributing the wind turbines into peer-clusters such that the wind turbines within each of the peer-clusters have similar environmental conditions. The methods and systems further include identifying a low performing wind turbine and a remaining portion of wind turbines within one of the peer-clusters based upon a predicted performance model, processing the component performance metrics of the low performing wind turbine, identifying a critical component of the low performing wind turbine and predicting the end of life of the critical component of the low performing wind turbine.