Wind Turbine Rotor Speed Control Using Linearized Resonance Dynamics
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Solution Overview
Problem
Wind turbines face challenges in controlling rotor speed to avoid exciting resonance frequencies, leading to vibrations and potential structural failure, especially due to manufacturing imperfections and aerodynamic imbalances, which conventional methods struggle to address efficiently within real-time control systems.
Innovation Solution
A method is introduced to transform the nonlinear system model of wind turbine components into a linear model, allowing for the inclusion of resonance dynamics in predictive control algorithms, which optimizes rotor speed control by penalizing operations that cause resonance vibrations, thereby preventing structural damage while maintaining power output efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a nonlinear resonance model is incorporated into the wind turbine model function for predictive control, then the accuracy of resonance prediction is improved, but the computational complexity and difficulty of real-time optimization increase significantly
Solution Approach 1:
The patent replaces the complex nonlinear resonance model with a simplified linear resonance model that is computationally inexpensive to evaluate. This simplified model, while less accurate than a full nonlinear model, provides sufficient precision for real-time control applications and can be rapidly evaluated within each control cycle without burdening the real-time optimization process
Solution Approach 2:
The patent transforms the resonance model from a nonlinear formulation to a linear formulation by changing the mathematical parameters and structure. This parameter transformation maintains the essential resonance prediction capability while reducing computational complexity, enabling the model to be efficiently integrated into the predictive control optimization framework
2Productivity
If the rotor speed passes rapidly through the exclusion zone to improve operating efficiency, then the power production is maximized, but the structural stress and vibrations increase due to resonance excitation
Solution Approach 1:
The patent uses the simplified linear resonance model to predict resonance conditions in advance within the predictive control framework. By evaluating the resonance risk before implementing control actions, the system can proactively adjust rotor speed to avoid excitation zones, preventing structural stress rather than reacting to it after occurrence
Solution Approach 2:
The patent modifies the conventional approach of rapidly skipping through exclusion zones by using predictive resonance assessment. When resonance is predicted, the control system deliberately slows the rotor speed transition through critical zones, sacrificing some transition speed to prevent resonance excitation and structural damage, thereby optimizing the balance between productivity and structural integrity
3Loss of time
If a simplified linear resonance model is used instead of a nonlinear model, then the computational efficiency is improved, but the prediction accuracy may be reduced
Solution Approach 1:
The patent employs a computationally inexpensive linear resonance model that can be rapidly evaluated multiple times within each control cycle. This simplified model trades some prediction accuracy for computational efficiency, enabling real-time implementation without requiring complex nonlinear calculations that would exceed real-time processing constraints
Solution Approach 2:
The patent transforms the resonance model parameters from a nonlinear formulation to a linear formulation, maintaining the essential resonance prediction capability while reducing computational burden. This parameter transformation allows the model to provide sufficiently accurate predictions for control purposes while enabling rapid evaluation within real-time optimization frameworks
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively prevents wind turbines from operating at rotor speeds that cause resonance vibrations, reducing structural stress and maintaining optimal power production by transforming the system model into a linear, convex form that can be efficiently solved in real-time, thus enhancing operational safety and efficiency.
Implementation Method 1
defining a system model describing resonance dynamics of a wind turbine component, where the system model has a nonlinear input term
Implementation Method 2
applying a transformation to the system model to obtain a transformed representation or model for a response oscillation amplitude of the wind turbine component, where the transformed model has a linear input term
Data Source
AI summary
Techniques for controlling rotor speed of a wind turbine. One technique includes defining a system model describing resonance dynamics of a wind turbine component, such as a wind turbine tower, where the system model has a nonlinear input term, e.g. a periodic forcing term. A transform is applied to the system model to obtain a transformed model for response oscillation amplitude of the wind turbine component, where the transformed model has a linear input term. A wind turbine model describing dynamics of the wind turbine is then defined, and includes the transformed model. A model-based control algorithm, e.g. model predictive control, is applied using the wind turbine model to determine at least one control output, e.g. generator torque, and the control output is used to control rotor speed of the wind turbine.