Wind Parameter Estimation Using Multi-Sensor Weighting
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Solution Overview
Problem
Existing wind turbine control systems face challenges in obtaining reliable wind parameter measurements due to sensor disturbances and malfunctions, leading to abnormal turbine responses, high loads, reduced power production, and unnecessary downtime, with current solutions being costly or requiring significant re-development.
Innovation Solution
A method and system that combines wind measurement signals from multiple sensors using statistical values and weighting factors to calculate a weighted sum, considering sensor status and operational parameters, to provide robust wind parameter values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If redundant wind sensors are installed on a wind turbine, then reliability of wind measurements is improved, but measurement precision deteriorates due to disturbances from nacelle components
Solution Approach 1:
The patent combines measurements from multiple wind sensors (at least two sensors) to determine wind parameter values. By merging the data from redundant sensors, the system maintains reliability while compensating for individual sensor disturbances through statistical processing and weighting mechanisms.
Solution Approach 2:
The system dynamically adjusts weighting factors assigned to each sensor based on measured operational parameters and statistical analysis. This parameter change allows the system to optimize measurement precision by giving higher weight to less disturbed sensors under varying operational conditions.
2Measurement precision
If wind sensors are relocated to avoid obstacles near the nacelle, then measurement precision is improved, but device complexity and development cost increase
Solution Approach 1:
The system performs self-calibration and automatic weighting adjustment based on operational data without requiring physical sensor relocation. The control system automatically adapts to sensor disturbances by analyzing measurement consistency and adjusting weighting factors, eliminating the need for complex re-installation and recalibration procedures.
Solution Approach 2:
The patent implements preliminary statistical analysis and weighting factor calculation during normal operation to preemptively compensate for sensor disturbances. This allows the system to maintain precision without physical relocation by preparing and applying correction weights before disturbances significantly impact control decisions.
3Measurement precision
If alternative wind measurement solutions such as lidar sensors are implemented, then measurement precision is improved, but cost and device complexity increase significantly
Solution Approach 1:
The system creates a virtual representation of accurate wind conditions by processing and weighting data from existing, simpler sensors. Instead of replacing physical sensors with expensive alternatives, the method generates reliable wind parameter values through computational processing of multiple lower-cost sensor inputs.
Solution Approach 2:
The patent replaces the need for complex alternative measurement systems (like lidar) with a software-based solution that uses statistical processing and weighting algorithms. This substitution achieves similar precision improvement without the substantial hardware investment and development effort required for alternative systems.
4Productivity
If measurements from faulty sensors are used, then productivity is maintained, but reliability deteriorates leading to abnormal turbine responses
Solution Approach 1:
The system dynamically adjusts weighting factors in real-time based on the performance and consistency of each sensor. When a sensor becomes faulty or disturbed, its weighting factor is automatically reduced, allowing the system to maintain productivity using remaining reliable sensors while protecting against abnormal responses from faulty data.
Solution Approach 2:
The control system continuously monitors measurement quality and uses feedback to adjust weighting factors. This feedback mechanism ensures that faulty sensor measurements do not compromise reliability while maintaining productivity by automatically adapting to sensor performance changes during operation.
Data Source
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AI summary
There is described a method of determining a wind parameter value for use in a wind turbine control system, the method comprising (a) receiving a plurality of wind measurement signals, wherein each wind measurement signal is provided by a respective wind sensor among a plurality of wind sensors, (b) determining a set of statistical values based on the wind measurement signals, (c) calculating a weighting factor for each wind measurement signal based on the set of statistical values, and (d) calculating the wind parameter value as a weighted sum by applying the calculated weighting factors to the corresponding wind measurement signals. Further, a corresponding system and a wind turbine with such a system are described.