Wind Turbine Component Replacement Schedule
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Solution Overview
Problem
Conventional methods for predicting the remaining useful lifetime (RUL) of wind turbine components do not effectively schedule maintenance or replacement, leading to suboptimal operation and power output.
Innovation Solution
A method to estimate the remaining producible energy (RGP) of wind turbine components, using machine-learning techniques such as recurrent neural networks, to determine the optimal replacement schedule based on energy production rather than temporal lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional remaining useful lifetime (RUL) prediction methods are used, then a temporal scale for component degradation is provided, but this does not enable appropriate scheduling of maintenance or replacement and does not allow optimized control of the wind turbine
Solution Approach 1:
The patent changes the fundamental parameter from temporal lifespan (RUL) to energy production capacity (RGP). Instead of predicting how many days or hours a component has left, the system estimates the actual energy production capacity remaining. This parameter transformation enables direct optimization of power output and maintenance scheduling based on actual functional capability rather than arbitrary time frames.
Solution Approach 2:
The patent replaces the conventional time-based prediction mechanism with an energy-based estimation system. By substituting the temporal metric with an energy production metric, the system achieves more accurate and actionable predictions for maintenance scheduling and operational optimization.
2Ease of operation
If fixed time frames are used for maintenance scheduling, then simplicity is maintained, but this leads to unnecessary downtime and suboptimal operation
Solution Approach 1:
The patent introduces dynamic maintenance scheduling based on actual component condition and remaining energy production capacity. Instead of fixed time frames, the system continuously updates maintenance recommendations based on real-time monitoring data, allowing flexible scheduling that adapts to actual component performance and minimizes unnecessary downtime.
Solution Approach 2:
The system implements feedback mechanisms where monitoring data about component health and energy production capacity continuously informs maintenance scheduling decisions. This closed-loop approach allows the system to adjust maintenance timing based on actual component condition, reducing unnecessary downtime while maintaining operational simplicity.
3Duration of action of stationary object
If component replacement is scheduled based on temporal lifespan, then a simple timeline is provided, but this does not account for varying operational conditions and energy production capabilities
Solution Approach 1:
The patent transforms the reliability assessment from time-based to energy-based parameters. By estimating remaining producible energy (RGP) instead of remaining useful lifetime, the system provides more accurate replacement timing that reflects actual component functional capability under varying operational conditions, thereby improving reliability assessment accuracy.
Data Source
AI summary
Provided is a method and arrangement of estimating replacement schedule of a, in particular mechanical, component of a wind turbine, the method comprising: estimating remaining producible energy until this component is to be replaced.