Wind Turbine Odometer Control for Fatigue Load Management
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Solution Overview
Problem
Conventional wind turbines face challenges in optimizing maintenance schedules due to unpredictable operational usage, leading to increased lifecycle costs and risks of under- or over-maintenance, without a system to efficiently manage fatigue and extreme loads.
Innovation Solution
An odometer-based supervisory control system that estimates cumulative damage to turbine components and adjusts operations to maximize energy production or minimize damage within predefined limits, using existing data and sensors to optimize maintenance and operational parameters dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If preventive maintenance actions are scheduled at predetermined time intervals, then the wind turbine can operate for a predetermined life, but the lifecycle cost increases due to significant maintenance costs and downtime
Solution Approach 1:
The maintenance schedule transitions from static predetermined intervals to dynamic condition-based intervals. The system continuously monitors operational usage parameters (fatigue loads, extreme loads, cycle counts) and adjusts maintenance timing based on actual component degradation rates, allowing extension of operating life between maintenance events when conditions permit.
Solution Approach 2:
The system implements closed-loop feedback by monitoring real-time operational parameters, comparing them against threshold values, and automatically adjusting maintenance schedules. Sensors detect fatigue accumulation and extreme load events, feeding this information back to the control system which then optimizes maintenance timing to minimize costs while ensuring safety.
2Ease of operation
If maintenance is performed at fixed intervals, then operational simplicity is maintained, but wind turbines with higher operational usage become under-maintained and more at risk for unplanned poor-quality events
Solution Approach 1:
The system changes the parameter basis for maintenance scheduling from fixed time intervals to usage-based parameters (fatigue cycles, extreme load counts, operational hours). This allows the maintenance interval to adapt automatically to actual component stress levels, ensuring high-usage turbines receive timely maintenance while simplifying low-usage operations.
Solution Approach 2:
The system performs preliminary assessment of component condition by continuously monitoring operational parameters and predicting remaining useful life. This allows proactive scheduling of maintenance before critical degradation occurs, preventing unplanned failures while maintaining operational simplicity through automated predictions.
3Productivity
If operational usage is increased to maximize energy production, then productivity improves, but fatigue and extreme loads accelerate life consumption
Solution Approach 1:
The system dynamically balances productivity and component life by adjusting operational parameters in real-time. When monitoring indicates approaching fatigue thresholds, the system automatically reduces power output or shuts down the turbine, preventing excessive life consumption while maximizing energy production during safe operating conditions.
Solution Approach 2:
The system implements periodic monitoring and assessment of fatigue accumulation, alternating between high-productivity operation modes and protective reduction modes. This periodic cycling allows the turbine to operate at full capacity during low-fatigue periods while preventing critical degradation through scheduled protective actions.
Data Source
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AI summary
A method for controlling a wind turbine connected to an electrical grid includes receiving, via a controller, a state estimate of the wind turbine. The method also includes determining, via the controller, a current condition of the wind turbine using, at least, the state estimate, the current condition defining a set of condition parameters of the wind turbine. Further, the method includes receiving, via the controller, a control function from a supervisory controller, the control function defining a relationship of the set of condition parameters with at least one operational parameter of the wind turbine. Moreover, the method includes dynamically controlling, via the controller, the wind turbine based on the current condition and the control function for multiple dynamic control intervals.