Wind Turbine Optimization Using Robustness Measurements
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Solution Overview
Problem
Existing wind turbine optimization methods are not robust enough to account for outliers in data points and seasonal variations, leading to sub-optimal operation.
Innovation Solution
A method and system that generate operating data for wind turbines, determine robustness measurements to identify outlier resistance, and calculate optimal set points based on power production, using sensors or computer models, and multi-objective optimization functions to maximize Annual Energy Production (AEP).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional optimization methods are used to set operational parameters, then the wind turbine can operate with default values, but the operation becomes sub-optimal due to outliers and seasonal variations
Solution Approach 1:
The patent changes the parameter used for optimization from traditional mean-based metrics to robustness measurements that are insensitive to outliers. Specifically, it uses measurements like standard deviation, interquartile range, or median absolute deviation to characterize operational parameters, thereby achieving both improved power production and reliability against outliers in wind speed and power curve data
Solution Approach 2:
The patent replaces traditional statistical optimization methods with a robust optimization framework that uses alternative mathematical approaches. Instead of relying on mean and variance that are sensitive to outliers, it substitutes robust statistical measures and optimization algorithms that explicitly account for and resist the influence of outlier data points
2Reliability
If robustness measurement is incorporated into optimization, then the system becomes resistant to outliers, but the computational complexity increases
Solution Approach 1:
The patent extracts and separates the robustness measurement calculation from the traditional optimization process. It identifies specific robustness metrics (such as standard deviation, interquartile range) that can be calculated independently from outlier-prone data, then uses these extracted robust measurements as inputs to the optimization algorithm, thereby reducing computational complexity while maintaining robustness
Solution Approach 2:
The optimization process is segmented into distinct stages: first calculating robustness measurements from historical data, then using these measurements to determine optimal operational parameters. This segmentation allows each stage to be optimized independently, reducing overall system complexity
Data Source
AI summary
The present subject matter is directed to a system and method for optimizing wind turbine operation. For example, the present disclosure is configured to generate operating data for at least one operational parameter of the wind turbine for a predetermined time period. The system can then determine a robustness measurement of at least a portion of the operating data. In general, the robustness measurement indicates the tendency of the operating data to be affected by outliers present in the operating data. In addition, the robustness measurement is typically a function of a distribution of the operating data. The present disclosure is then configured to determine at least one optimal set point for the operational parameter as a function of the robustness measurement and a power production of the wind turbine. The wind turbine can then be operated based on the optimal set point.