Wind Turbine Control Combining MPC and Non-Optimizing Routines
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Solution Overview
Problem
Modern wind turbines face challenges in implementing advanced model predictive control (MPC) due to the high computational demands required for real-time control, which are often not met by the available computing power in modern turbines.
Innovation Solution
A wind turbine control system comprising a first MPC controller unit for calculating a predicted operational trajectory and a second non-optimizing controller unit for calculating a second control value, with an actuator controller unit combining these values to generate actuator control signals, allowing for real-time control without excessive computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If model predictive control (MPC) is implemented for real-time wind turbine control, then control performance and power capture are improved, but computational requirements exceed available computing power
Solution Approach 1:
The control system is segmented into multiple independent controller units, each responsible for specific control functions. The MPC controller handles optimization-critical functions while simpler controllers manage other aspects, allowing computational tasks to be divided and distributed across available processing resources.
Solution Approach 2:
MPC is applied partially to only those control variables where optimization provides significant benefit, rather than implementing full MPC across all control functions. This selective application reduces computational burden while maintaining power capture improvements in critical areas.
2Productivity
If advanced MPC algorithms are used for all control variables, then overall control optimization is improved, but device complexity and computational load increase
Solution Approach 1:
The control system is divided into multiple independent controller units, each handling specific control functions with appropriate complexity levels. This segmentation allows advanced MPC to be applied only where necessary while simpler control strategies handle other functions, reducing overall system complexity.
Solution Approach 2:
Different control variables are treated with different levels of control sophistication. MPC is applied to variables where optimization provides significant benefit, while simpler control strategies are used for other variables, creating local quality variations in control approach matched to local needs.
Data Source
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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.