Wind Turbine Constraint Scheduling for Gust-Responsive Control
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Solution Overview
Problem
Existing wind turbine control methods using predictive control do not effectively manage rapidly changing operating conditions, as constraints are often fixed and do not adapt to current operational parameters, leading to suboptimal performance during wind gusts or changes in wind direction.
Innovation Solution
A method that predicts the behavior of wind turbine components over a prediction horizon using a wind turbine model, determining behavioral constraints based on operational parameters like wind speed and control settings, and optimizing a cost function subject to these scheduled constraints to determine control outputs, ensuring optimal operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed constraints are used in predictive control optimization, then the control problem is simpler to solve, but the control performance deteriorates during rapidly changing operating conditions
Solution Approach 1:
The patent applies dynamics by making the constraints dynamic rather than fixed. The behavioral constraints are determined based on predicted behavior of wind turbine components over a prediction horizon, allowing the constraints to adapt to changing operating conditions. This resolves the contradiction by enabling the control system to maintain optimal performance during rapid changes while keeping the optimization problem computationally tractable through structured constraint formulation.
Solution Approach 2:
The patent applies preliminary action by predicting the behavior of wind turbine components over a prediction horizon before determining the constraints. This forward-looking approach allows the control system to anticipate future states and set appropriate constraints in advance, improving responsiveness to rapidly changing conditions while maintaining computational efficiency through the structured prediction-based constraint formulation.
2Adaptability or versatility
If behavioral constraints are determined based on predicted operational parameters, then the adaptability to changing conditions improves, but the computational complexity increases
Solution Approach 1:
The patent makes the constraint determination process dynamic by using predicted operational parameters from a wind turbine model. The behavioral constraints are updated based on the predicted behavior over a prediction horizon, allowing the system to adapt to changing conditions. The complexity is managed through the structured model-based prediction approach and efficient optimization algorithms.
Solution Approach 2:
The patent applies self-service by using the wind turbine's own model and predicted behavior to determine its operational constraints. The system uses its internal model to predict future states and automatically sets appropriate behavioral constraints without external intervention, improving adaptability while managing complexity through self-contained model-based determination.
3Speed
If model predictive control with scheduled constraints is used, then the reaction to rapid changes improves, but the computational burden increases
Solution Approach 1:
The patent applies preliminary action by performing predictions over a prediction horizon and determining constraints in advance before the optimization step. This forward-looking approach enables faster response to rapid changes because the system has already anticipated future states. The computational burden is managed by efficiently structuring the prediction and constraint determination processes to be completed within the control cycle.
Solution Approach 2:
The patent uses dynamic model-based prediction to anticipate future states and determine time-varying behavioral constraints. This dynamic approach improves response speed to rapid changes by proactively adjusting constraints based on predicted operational parameters. The computational efficiency is achieved through the structured formulation of the prediction and optimization problem.
Data Source
AI summary
The invention provides a method for controlling a wind turbine, including predicting behaviour of one or more wind turbine components such as a wind turbine tower over a prediction horizon using a wind turbine model that describes dynamics of the one or more wind turbine components or states. The method includes determining behavioural constraints associated with operation of the wind turbine, wherein the behavioural constraints are based on operational parameters of the wind turbine such as operating conditions, e.g. wind speed. The method includes using the predicted behaviour of the one or more wind turbine components in a cost function, and optimising the cost function subject to the determined behavioural constraints to determine at least one control output, such as blade pitch control or generator speed control, for controlling operation of the wind turbine.