Multivariable Wind Turbine Control Under Disturbance Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional controllers for power generating assets, such as wind turbines, often consist of single input/single output loops, leading to conflicting control signals and suboptimal power production due to environmental disturbances, making it difficult to determine the appropriate command signal.
Innovation Solution
A method and system utilizing a consolidated multivariable controller with an H-infinity (H∞) module for real-time gain computation, incorporating input and acceleration constraints, and integrating second derivatives to generate control vectors, which are used to adjust the operating state of components like blade pitch and generator torque.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional SISO controllers are used for power generating assets, then the control system structure is simple and easy to implement, but conflicting control signals are generated and power production becomes suboptimal due to environmental disturbances
Solution Approach 1:
The patent merges multiple SISO controllers into a unified MIMO controller that processes multiple inputs and outputs simultaneously. The controller integrates blade pitch angle control, generator torque control, and other subsystem controls into a single coordinated system, eliminating conflicting control signals and optimizing power production through multivariable control strategies.
Solution Approach 2:
The MIMO controller performs multiple control functions simultaneously, including disturbance attenuation, constraint satisfaction, and optimal power extraction. It universally handles various environmental conditions and operational constraints through a single control architecture, making the system adaptable to different operating scenarios without requiring separate specialized controllers.
2Reliability
If MIMO control with H-infinity module is implemented, then optimal power production and disturbance attenuation are achieved, but computational complexity increases requiring real-time gain computation
Solution Approach 1:
The controller pre-computes gain schedules offline for various operating conditions and stores them in lookup tables. During real-time operation, the controller rapidly retrieves pre-computed gains based on current operating parameters, avoiding the need for complex real-time H-infinity optimization calculations while maintaining optimal disturbance attenuation performance.
Solution Approach 2:
The controller uses gain scheduling with parameter-based lookup tables, where control gains are selected based on operating parameters such as wind speed, rotor speed, and generator power. This transforms the continuous complex optimization problem into a discrete parameter selection problem, significantly reducing real-time computational complexity while maintaining reliability.
3Reliability
If rate and acceleration constraints are incorporated into control design, then operational safety and component protection are improved, but control signal flexibility is reduced
Solution Approach 1:
The controller dynamically adjusts control signals to satisfy rate and acceleration constraints while maintaining adaptability. It incorporates derivative terms and constraint handling mechanisms that allow the system to respond flexibly to changing conditions within safe operational boundaries, transforming rigid constraints into dynamic guidance for control signal generation.
Solution Approach 2:
The controller uses feedback mechanisms to monitor control signal rates and accelerations, automatically adjusting commands to satisfy constraints while maintaining optimal performance. The feedback loop ensures that constraint violations are detected and corrected in real-time, preserving both safety and flexibility through continuous adaptation.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Systems and methods are provided for the robust, multivariable control of a power generating asset via H-infinity loop shaping using coprime factorization. Accordingly, a controller of the power generating asset computes a gain value for an H-infinity (H∞) module in real-time at predetermined sampling intervals using an actuator dynamic model. The controller then determines an acceleration factor based, at least in part, on the gain value of the H∞ module. Based, at least in part on the acceleration vector, the controller generates a control vector. An operating state of at least one component of the power generating asset is changed based on the control vector.