Wind Turbine Feedback for Atmospheric Turbulence Forecasting
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Solution Overview
Problem
Current wind resource and wind farm output forecasting methods based on traditional numerical weather prediction are insufficient for accurate power output commitments, leading to high penalties due to inadequate parameterization of atmospheric turbulence.
Innovation Solution
A high-resolution numerical weather prediction method that utilizes real-time wind-speed measurements from each wind turbine, combined with other meteorological data, to parameterize turbulence and improve forecasting accuracy, especially by using spatially distributed wind turbines and additional meteorological data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional numerical weather prediction models are used for wind resource forecasting, then the forecasting system is simple to operate, but the forecast accuracy is insufficient for high-value power output commitments
Solution Approach 1:
The patent implements a feedback mechanism where actual wind speed measurements from the wind farm are continuously fed back to adjust and calibrate the numerical weather prediction model. This allows the model to learn from real-world data and improve its predictions over time, resolving the contradiction by enhancing accuracy through adaptive feedback without requiring a complete system overhaul
Solution Approach 2:
The patent changes key parameters of the forecasting system by incorporating real-time wind speed measurements from multiple wind turbines and using these to dynamically adjust turbulence parameterization. This transforms the static NWP model into a dynamic system that adapts to actual conditions, improving forecast accuracy while maintaining the underlying model structure
2Measurement precision
If high-resolution numerical weather prediction with turbulence parameterization is implemented, then forecast accuracy improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent makes the wind turbines serve multiple functions: they generate electricity and simultaneously act as distributed measurement instruments for wind speed monitoring. This multi-functionality provides the necessary data for high-resolution turbulence parameterization without requiring separate dedicated measurement infrastructure, thus improving accuracy while managing complexity
Solution Approach 2:
The wind farm itself provides the measurement data needed for forecasting by using its operational wind turbines as sensors. The system serves its own forecasting needs through the measurements already being taken for power generation, eliminating the need for external measurement systems and reducing overall system complexity
3Measurement precision
If wind speed measurements from multiple wind turbines are collected and combined, then turbulence description precision improves, but data processing and model calibration complexity increases
Solution Approach 1:
The patent segments the wind farm into multiple measurement points at different locations and heights, with each wind turbine providing localized wind speed data. This segmentation allows for detailed spatial characterization of turbulence patterns while maintaining manageable data processing through distributed measurement
4Measurement precision
If continuous real-time wind speed measurements are taken at hub height from multiple turbines, then forecast quality improves, but measurement and data collection infrastructure complexity increases
Solution Approach 1:
The wind turbines perform dual functions as both power generation devices and wind speed measurement instruments. The anemometers already installed on the turbines for operational control are utilized for forecasting purposes, eliminating the need for separate measurement infrastructure and reducing overall system complexity while maintaining high measurement precision
Data Source
AI summary
A method and an arrangement for a forecast of wind-resources of a wind-farm are provided. The forecast is done by a numerical weather-prediction-tool, the weather-prediction-tool using a long-term data-set of meteorological data. The data are related to the location of the wind-farm. A wind-speed measurement is done by a wind-turbine of the wind-farm to do a parameterization of an atmospheric turbulence. The wind-speed measurement is used to generate a data-stream, which is combined with the data-set of the meteorological data to do the forecast.