Wind Turbine Failure Forecasting With Cohort-Based Model Selection
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Solution Overview
Problem
Existing systems struggle to accurately predict failures in renewable energy assets, such as wind turbines, with high lead time and accuracy, due to the complexity of data from multiple sensors and the scalability issues of prior art systems.
Innovation Solution
A centralized system that utilizes a model selection pipeline to receive event and alarm data from SCADA systems, create cohort instances, generate feature matrices, and extract patterns, followed by a model training and testing pipeline to develop failure prediction models using historical sensor data and patterns of events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior art systems are used to predict wind turbine failures, then detection of failures can be performed, but accuracy is low and lead time is insufficient
Solution Approach 1:
The system segments the wind turbine operation into distinct cohorts based on controller type and geographical location. Each cohort is analyzed separately to capture site-specific patterns and behaviors, improving prediction accuracy while maintaining sufficient lead time by focusing computational resources on relevant subsets of data.
Solution Approach 2:
The system transforms the prediction approach by adding temporal dimensions through observation time windows and lead time windows. By analyzing historical sensor data across multiple time windows and comparing patterns, the system achieves both high accuracy and extended lead time for failure predictions.
2Productivity
If centralized system with multiple pipelines is implemented, then accuracy and scalability are improved, but device complexity increases
Solution Approach 1:
The system architecture is segmented into three distinct pipelines: model selection, model training and testing, and model scoring. Each pipeline performs a specific function and can be developed, maintained, and scaled independently, improving overall system productivity while managing complexity through functional separation.
Solution Approach 2:
The centralized system design creates universal pipelines that can process data from multiple wind turbines across different cohorts simultaneously. The same model selection and training processes serve all cohorts, improving scalability without proportionally increasing complexity through reuse of computational frameworks.
3Measurement precision
If cohort-based analysis is performed, then prediction accuracy for specific wind turbine types is improved, but computational burden increases
Solution Approach 1:
By segmenting the overall population into smaller cohorts based on controller type and location, the system improves prediction accuracy for each cohort while reducing computational burden compared to analyzing all data uniformly. Each cohort requires less computational resources than a comprehensive analysis, and results can be aggregated for overall insights.
Solution Approach 2:
The system applies partial action by focusing computational resources on specific cohorts and time windows relevant to each prediction task. Rather than continuously analyzing all historical data for all turbines, the system selectively processes data within defined observation and lead time windows, reducing energy consumption while maintaining accuracy.
Data Source
AI summary
An example method utilizing different pipelines of a prediction system, comprises receiving event and alarm data from event logs, failure data, and asset data from Supervisory Control and Data Acquisition (SCADA) system(s), retrieving patterns of events, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receiving current sensor data from the sensors of the components, applying the selected model(s) to the current sensor data to generate a component failure prediction, comparing the component failure prediction to a threshold, and generating an alert and report based on the comparison to the threshold.