Wind Turbine Rotor Speed Control for Wide-Range MPPT
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Solution Overview
Problem
Existing wind turbine control systems face challenges in efficiently tracking maximum power at varying wind speeds due to the intermittence of wind speed, leading to suboptimal power generation and increased costs with existing MPPT control methods, which lack precision and efficiency.
Innovation Solution
A neural network-based control system for wind turbines that considers wind speed, blade pitch angle, tip speed ratio, and other parameters to determine the optimum rotor speed and maximum power output, using machine learning algorithms to adjust control strategies in real-time, enabling efficient power tracking across a wide range of wind speeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional MPPT control methods are used, then the system is simple to implement, but the power generation efficiency is suboptimal due to inability to precisely track maximum power at varying wind speeds
Solution Approach 1:
The system pre-calculates and stores the optimal rotor speed corresponding to each wind speed in a lookup table during system initialization or offline operation. When the wind speed changes, the controller directly retrieves the pre-computed optimal speed from the table, eliminating the need for real-time complex optimization calculations. This preliminary preparation enables fast response to wind speed variations while maintaining relatively simple real-time control logic.
Solution Approach 2:
The patent replaces complex real-time mechanical control algorithms with a data-driven approach using pre-computed lookup tables. Instead of implementing complex iterative optimization algorithms during real-time operation, the system substitutes these with straightforward table-based retrieval and interpolation, significantly reducing computational burden and improving response speed while maintaining high power tracking efficiency.
2Productivity
If the wind turbine operates across a wide wind speed range, then the energy capture is maximized, but the control precision decreases due to the intermittence and variability of wind speed
Solution Approach 1:
The wide operating wind speed range is divided into multiple discrete segments or intervals, with each segment having its own pre-computed optimal rotor speed values stored in the lookup table. This segmentation allows the control system to maintain high precision within each narrow wind speed interval while covering the entire operational range. The table stores optimized parameters for each segment, enabling accurate power tracking even as wind conditions vary across the full spectrum.
Solution Approach 2:
The system dynamically adjusts the rotor speed parameter based on changing wind speed conditions by retrieving appropriate values from the lookup table. The optimal rotor speed is not fixed but changes according to the current wind speed, allowing the turbine to adapt to varying wind conditions while maintaining maximum power extraction efficiency across the entire operational range.
3Speed
If fast response to wind speed changes is implemented, then the power tracking efficiency is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The system pre-calculates and stores the optimal rotor speed corresponding to each wind speed in a lookup table during system initialization or offline operation. When the wind speed changes, the controller directly retrieves the pre-computed optimal speed from the table, eliminating the need for real-time complex optimization calculations. This preliminary preparation enables fast response to wind speed variations while maintaining relatively simple real-time control logic.
Solution Approach 2:
Instead of performing complex real-time calculations, the system creates a simplified copy or representation of the optimal control strategy in the form of a lookup table. This table contains pre-computed optimal rotor speeds for various wind conditions, serving as a simplified model that can be queried instantly. The copying approach trades offline computational effort for online speed, achieving fast response without complex real-time algorithms.
Data Source
AI summary
A wind turbine control apparatus, method and non-transitory computer-readable medium are disclosed. The wind turbine control apparatus comprises a generator connected to a wind turbine with a drive train. The drive train comprises a rotor, a low speed shaft, a gear box, a high speed shaft, and a controller module. The controller module is configured to obtain a maximum power within a large range of varying wind velocities by operating the rotor at a neural network determined optimal angular speed for the current wind velocity.