Detecting Asymmetric Icing via Lateral Tower Acceleration
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Solution Overview
Problem
Current systems for detecting asymmetric icing on wind turbine rotor blades require additional hardware, increasing installation and operational costs, and are impractical for areas with average yearly temperatures above freezing.
Innovation Solution
A software-based method that utilizes existing wind turbine hardware, specifically lateral tower acceleration data, to detect asymmetric icing without the need for additional sensors, by monitoring and analyzing vibration data to determine rotor-mass imbalance and potential icing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional hardware such as sensors, anemometers, or piezoelectric transducers is installed to detect asymmetric icing, then detection accuracy is improved, but installation and operational costs increase
Solution Approach 1:
The invention repurposes existing wind turbine hardware (accelerometers, anemometers, temperature sensors) originally designed for other monitoring functions to also detect asymmetric icing. This multi-functional use eliminates the need for dedicated icing detection hardware, reducing costs while maintaining detection capability
Solution Approach 2:
The wind turbine's existing monitoring system serves itself by detecting icing conditions through data already being collected for other purposes. The system uses its own operational data (vibration, wind speed, temperature) to identify icing without requiring external specialized equipment
2Reliability
If additional hardware is installed for asymmetric icing detection, then detection capability is improved, but operational costs increase
Solution Approach 1:
Existing sensors perform multiple functions including structural health monitoring, vibration analysis, and icing detection. This eliminates the need for separate dedicated icing sensors, reducing both capital and operational expenditures while maintaining reliable detection
3Measurement precision
If additional hardware is required for asymmetric icing detection, then detection precision is improved, but applicability to warm climates is reduced
Solution Approach 1:
The system uses existing multi-purpose sensors that can detect icing conditions through vibration patterns and environmental data regardless of climate zone. This approach allows deployment in both cold and warm climates where icing may occur intermittently, increasing geographical versatility
Solution Approach 2:
The system continuously monitors environmental conditions and vibration patterns, detecting icing conditions as they develop rather than waiting for confirmed icing events. This early detection capability is particularly valuable in warm climates where icing occurs less frequently and more unpredictably
4Device complexity
If existing wind turbine hardware is used for asymmetric icing detection, then cost is reduced, but detection capability must be achieved through data analysis
Solution Approach 1:
The invention replaces physical dedicated sensing hardware with computational analysis of existing sensor data. Instead of installing specialized mechanical icing detection devices, the system uses software algorithms to interpret vibration, wind speed, and temperature data for icing detection
Solution Approach 2:
Data processing algorithms serve as an intermediary, translating existing sensor measurements into icing detection information. These computational mediators extract icing-related signals from multi-purpose sensor data without requiring specialized hardware
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
Enables cost-effective detection of asymmetric icing using existing wind turbine components, reducing operational expenses and allowing for continuous monitoring of wind turbines in various climates, thereby preventing rotor-mass imbalance and fatigue loads.
Implementation Method 1
lateral tower acceleration data to detect asymmetric icing
Implementation Method 2
Asymmetric icing may also yield a rotor-mass imbalance leading to higher fatigue loads
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
A method 200 and system 180 for detecting asymmetric utilizing lateral tower 110acceleration data may include: providing a lateral tower acceleration monitoring system 180; determining from the lateral tower acceleration monitoring system whether a lateral tower acceleration is above an acceleration limit; determining whether a rotor-mass imbalance condition exists; and determining whether the lateral tower acceleration coincides with icing on a rotor.