Wind Turbine Control System Dynamic De-rating
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Solution Overview
Problem
Conventional wind turbine control strategies often result in a loss of potential power production due to inadequate adjustment of de-rating in response to improved wind conditions, leading to mechanical loads exceeding design limits.
Innovation Solution
A system and method utilizing sensors, such as Micro Inertial Measurement Units, to detect loading conditions and determine correction parameters, allowing for real-time adjustments in pitch angle, generator torque, and power output to optimize power production while maintaining loads within design limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional de-rating strategies are applied to maintain mechanical loads within design limits, then reliability is improved, but power output is reduced
Solution Approach 1:
The control system dynamically adjusts the de-rating level based on real-time monitoring of mechanical loads and wind conditions. Instead of applying a fixed de-rating, the system continuously adapts the power output reduction to match actual turbulence intensity and load conditions, allowing maximum power extraction when conditions permit while maintaining reliability when loads approach design limits
Solution Approach 2:
The system implements a feedback control mechanism where mechanical load measurements from sensors are fed back to the controller, which then adjusts the generator torque and blade pitch accordingly. This closed-loop control ensures that power output is optimized while maintaining mechanical loads within safe operating boundaries through continuous monitoring and adjustment
2Reliability
If fixed de-rating is applied regardless of wind conditions, then mechanical loads are controlled, but productivity is reduced due to loss of potential power production
Solution Approach 1:
The system changes the operational parameters (power setpoint, blade pitch angle, generator torque) based on measured turbulence intensity and wind conditions. When turbulence is low and loads are well below design limits, the system increases power output parameter to maximize productivity. When turbulence increases and loads approach design limits, the system adjusts parameters to reduce power output and maintain load control
Solution Approach 2:
The de-rating level is made dynamic rather than fixed, adapting in real-time to changing wind conditions and turbulence intensity. The system continuously adjusts the relationship between available wind power and actual power extraction based on current mechanical load conditions, optimizing the balance between reliability and productivity for each moment in time
3Reliability
If rapid adjustment to de-rating is implemented in response to extreme gusts, then mechanical integrity is protected, but power output is reduced due to premature de-rating
Solution Approach 1:
The system applies preliminary anti-action by implementing anticipatory control actions based on predicted load trends rather than waiting for loads to actually reach critical levels. The controller uses rate-of-change detection and predictive algorithms to apply gentle power reduction before extreme loads occur, preventing mechanical stress while minimizing power loss compared to reactive de-rating
Solution Approach 2:
The system takes preliminary action by continuously monitoring wind conditions and preparing control adjustments in advance. When turbulence intensity increases or load trends indicate approaching design limits, the system proactively adjusts power output before critical loads occur, maintaining mechanical integrity while reducing the severity and duration of power reductions
Data Source
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AI summary
Systems and methods 800 for controlling a wind turbine 10 are disclosed. The method includes: measuring 802 a loading condition acting on the wind turbine 10; determining 804 a first scaler factor based on the measured loading condition; determining 806 a correction parameter for the wind turbine 10, the correction parameter being a function of at least two measured operating conditions and representative of a real-time operational state of the wind turbine; determining 808 a second scaler factor based on the correction parameter; calculating 810 an adjustment set point based on the first scaler factor and the second scaler factor; and, controlling 812 the wind turbine 10 based on the adjustment set point.