Wind Turbine Controller Parameterization for Icing Conditions
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Solution Overview
Problem
Wind energy installations experience power losses and increased load when rotor blades ice up, as existing control systems do not account for changes in mass and aerodynamics, leading to suboptimal operation.
Innovation Solution
A method for parameterizing wind energy installation controllers to account for icing conditions by adjusting blade pitch angles, generator braking torque, and heating, using artificial intelligence to determine optimal parameter values based on predicted power, load, and downstream flow, allowing for adaptive control across various icing states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the controller uses fixed control parameters for non-iced-up conditions, then the control system is simple and easy to operate, but power output decreases and load increases when icing occurs
Solution Approach 1:
The controller transitions from static fixed parameters to dynamic adaptive parameters that automatically adjust based on detected icing conditions. The system dynamically modifies control parameters including blade pitch angles and generator braking torque based on real-time icing state detection, allowing optimal performance across varying operational conditions without requiring manual reconfiguration.
Solution Approach 2:
The system changes control parameters (blade pitch angles, generator braking torque) based on detected icing states. The controller stores multiple sets of control parameters corresponding to different icing conditions and automatically selects appropriate parameter sets, enabling adaptation to changing environmental conditions while maintaining straightforward operation through automated parameter selection.
2Reliability
If the controller adapts to different icing states with multiple parameter sets, then power output improves and load reduces, but the complexity of parameterization increases
Solution Approach 1:
The controller automatically detects icing conditions and self-adjusts control parameters without requiring external intervention or complex manual configuration. The system monitors operational data, identifies icing states, and autonomously selects appropriate control parameter sets, reducing the burden on operators while improving reliability under varying icing conditions.
Solution Approach 2:
The system implements feedback mechanisms that monitor operational parameters and detect icing conditions in real-time. Based on this feedback, the controller automatically adjusts control parameters to maintain optimal performance and reliability, creating a closed-loop control system that adapts to changing conditions while maintaining straightforward operation.
3Force
If control parameters remain unchanged during icing, then the control system is simple to operate, but aerodynamic performance deteriorates and mechanical load increases
Solution Approach 1:
The controller automatically detects increased mechanical load caused by icing and self-adjusts control parameters to compensate. The system monitors load indicators and autonomously modifies blade pitch angles and generator torque to reduce mechanical stress, maintaining ease of operation while protecting the installation from excessive loads during icing events.
Data Source
AI summary
A method of parameterizing a controller of a first wind energy installation wherein the controller sets a manipulated variable of the wind energy installation as a function of an input variable. An artificial intelligence determines at least one value of a parameter of the controller for at least one state/degree of being iced up of the wind energy installation based on a power curve, load, and/or downstream flow of the wind energy installation predicted with a mathematical model of the wind energy installation for at least one state/degree of being iced up, and/or determines at least one value of a parameter of the controller for at least one state/degree of being iced up of the wind energy installation, based on at least one determined state/degree of being iced up and a power, load, and/or downstream flow of the wind energy installation and/or at least one second wind energy installation.