Wind Turbine Component Mounting via Acceleration Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current wind turbine assembly methods rely heavily on experience and wave/movement data, lacking precise prediction tools to ensure safe and efficient installation, particularly due to unpredictable disturbance accelerations that can cause harmful movements during component assembly.
Innovation Solution
The method employs acceleration sensors to determine disturbance and assembly accelerations, using mathematical forecasting to predict future interference and assembly conditions, allowing for optimized assembly by comparing these predictions with threshold values to ensure safe installation without active crane tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time sensor data from acceleration sensors is used to predict future disturbance accelerations, then assembly safety and reliability are improved, but device complexity and measurement requirements increase
Solution Approach 1:
The system performs preliminary prediction of future disturbance accelerations using mathematical methods (e.g., autoregressive models) based on current and past sensor data. This allows the assembly process to be optimized in advance by comparing predicted values with threshold values before actual assembly operations commence, ensuring safety without requiring complex real-time intervention systems
Solution Approach 2:
A prediction algorithm acts as an intermediary between raw acceleration sensor data and assembly decision-making. The mathematical prediction model processes sensor inputs and generates forecasted disturbance values, which are then compared against predefined thresholds to determine whether assembly should proceed, pause, or be aborted, simplifying the overall control architecture
2Productivity
If mathematical prediction methods are used to forecast future disturbance accelerations, then assembly process optimization is improved, but loss of time for data processing and prediction increases
Solution Approach 1:
The system uses partial prediction by focusing only on the essential disturbance acceleration components relevant to assembly safety, rather than attempting to predict all possible motion parameters. This selective approach reduces computational complexity and processing time while maintaining sufficient accuracy for assembly decision-making
Solution Approach 2:
Complex mechanical real-time tracking systems are replaced with mathematical prediction algorithms that process sensor data computationally. This substitution reduces mechanical complexity and allows for faster processing of disturbance predictions, improving both productivity and reducing time delays
3Device complexity
If active crane tracking is replaced with forecast-based assembly decisions, then device complexity is reduced, but measurement precision requirements increase
Solution Approach 1:
The complex active crane tracking system is extracted and replaced with a simpler forecast-based decision system. Only essential acceleration data from fixed sensors on the turbine structure is utilized, rather than requiring continuous tracking of all moving components. This extraction simplifies the overall system while maintaining safety through predictive threshold comparison
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach simplifies the assembly process by providing reliable, real-time predictions of safe assembly conditions, reducing the risk of damage and inaccuracies by avoiding harmful movements, thus enhancing safety and reliability.
Implementation Method 1
an installed component has a first acceleration sensor for recording an acceleration
Data Source
Figure 1~5
AI summary
The invention relates to a method for optimizing the mounting of a component to be installed on a wind turbine, wherein an installed component has a first acceleration sensor for detecting an acceleration, comprising the following steps: - Determining a disturbance acceleration and/or a quantity of the installed component derived from the disturbance acceleration in one or more disturbance axes using the first acceleration sensor, - Predicting a future disturbance acceleration value or a quantity derived therefrom along the disturbance axis or axes using a mathematical method, such that a first predicted value for the future disturbance acceleration and/or a quantity derived therefrom of the installed component is available, - Comparing the first predicted value with a first threshold value for an acceptable disturbance acceleration or a quantity derived therefrom along the disturbance axis or axes.- Mounting the component to be installed, in case the predicted value is below the threshold, so that the mounting is carried out in an optimized manner.