Wind Turbine Control Using MPC and Non-Optimizing Routines
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Solution Overview
Problem
Wind turbine control systems face challenges in implementing advanced model-based real-time control due to computationally intensive Model Predictive Control (MPC) algorithms, which require significant computing power not always available in modern turbines.
Innovation Solution
A wind turbine control system comprising a combination of an MPC controller unit for calculating predicted control values and a non-optimizing controller unit for determining additional control values, with an actuator controller unit combining these signals to generate actuator control signals, allowing for real-time control without relying on excessive computing power. This system enables the selection of control routines based on the specific control tasks and available computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Model Predictive Control (MPC) algorithms are implemented for real-time control, then control performance and optimization capability are improved, but computing power requirements increase significantly
Solution Approach 1:
The control system is divided into multiple controller units, each implementing different control routines (MPC and non-optimizing). This segmentation allows the computationally intensive MPC algorithm to be applied only to specific control tasks where it provides the most benefit, while other tasks are handled by simpler controllers, thereby reducing overall computing power requirements while maintaining improved control performance for critical parameters.
2Measurement precision
If advanced MPC control techniques are applied, then control precision and optimization are improved, but device complexity increases
Solution Approach 1:
The controller is segmented into multiple independent controller units, each with a specific control routine. This modular architecture allows advanced MPC techniques to be applied selectively to specific control tasks requiring high precision, while other tasks use simpler control logic. The segmentation reduces overall system complexity by distributing functions across specialized units rather than requiring one complex controller to handle all tasks.
Solution Approach 2:
The control system implements a universal architecture where multiple controller units can handle different control tasks. Each controller unit is designed to be self-contained and can operate independently or in coordination with others, allowing the system to adapt to different control requirements without increasing individual unit complexity. This multi-functionality approach enables precise control where needed while maintaining simplicity elsewhere.
3Speed
If multiple controller units are used to distribute computational tasks, then real-time control capability is improved, but system complexity increases
Solution Approach 1:
The control system is segmented into multiple specialized controller units that can operate in parallel. Each unit handles specific computational tasks, enabling real-time control by distributing the computational load. The segmentation is designed so that each controller unit has a well-defined function, which reduces the complexity of inter-controller communication and coordination compared to a monolithic controller attempting to handle all tasks sequentially.
Data Source
AI summary
Wind turbine control based on optimizing and non-optimizing controller routines is disclosed. A first controller implements a model predictive control (MPC) routine for calculating a predicted first control value. A second controller implements a non-optimizing control routine for calculating a second control value. An actuator controller unit determines an actuator control signal by combining the predicted first control value and the second control value.