Wind Turbine Control Using Wind Speed Forecasting
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Solution Overview
Problem
Wind turbines face inefficiencies due to slow reaction times to changing wind conditions, leading to suboptimal energy capture and increased loads, often resulting in unnecessary shutdowns during transient gusts.
Innovation Solution
A method and system that utilize a wind speed estimator and forecaster to create probability density functions of future wind speeds, allowing for anticipatory adjustment of operating parameters such as rotor speed and blade pitch angles to mitigate loads and improve energy capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If feedback control is used to adjust rotor speed and pitch angles, then power output is optimized and loads are maintained within acceptable limits, but the reaction time is slow due to large inertias and actuator limitations
Solution Approach 1:
The system forecasts future wind speeds using probability density functions before the actual wind conditions occur. By predicting wind conditions in advance and pre-adjusting operating parameters, the system eliminates the lag inherent in feedback control, allowing the turbine to react to wind changes before they fully manifest rather than after
Solution Approach 2:
A wind speed forecast system acts as an intermediary between the actual wind conditions and the turbine control system. This intermediary processes wind data through statistical modeling and probability density functions to generate predictive information, which then guides control decisions, bridging the gap between slow physical response and the need for timely adaptation
2Strength
If the wind turbine shuts down during extreme wind conditions, then excessive loads and damage are prevented, but power loss occurs during transient gusts that would not require shutdown
Solution Approach 1:
The system forecasts future wind speeds and compares them against shutdown thresholds in advance. By predicting whether extreme conditions will actually occur and persist, the system can avoid unnecessary shutdowns during transient gusts while still protecting against genuine extreme events, making shutdown decisions proactively rather than reactively
Solution Approach 2:
The system uses probability density functions to transform raw wind speed data into probabilistic forecasts. By analyzing the distribution and likelihood of future wind speeds rather than relying on simple threshold comparisons, the system can distinguish between transient gusts and sustained extreme conditions, adjusting operating parameters accordingly to avoid unnecessary shutdowns
3Productivity
If the turbine reacts quickly to wind changes, then energy capture is optimized, but higher loads are encountered due to rapid adjustments
Solution Approach 1:
By forecasting wind conditions in advance, the system can plan parameter changes smoothly over time rather than making abrupt adjustments. This allows optimization of energy capture while distributing mechanical stress over a longer period, reducing peak loads on the turbine components
Solution Approach 2:
The system dynamically adjusts operating parameters based on forecasted wind conditions and probability density functions. Rather than using fixed or purely reactive control, the system continuously adapts rotor speed and pitch angles according to predicted wind evolution, optimizing the balance between energy capture and load management
Data Source
AI summary
A method of operating a wind turbine comprises obtaining current wind speed, forecasting wind speeds by creating probability density functions of wind speeds at a series of time points in the future based on the obtained current wind speed and past wind speeds, determining operating parameters of the wind turbine for the forecasted wind speeds, and controlling the wind turbine based on the determined operating parameters.