Wind Turbine MIMO Control With SDQR Gain Scheduling
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Solution Overview
Problem
Conventional wind turbine controllers using single input/single output (SISO) loops face challenges in guaranteeing control signal materialization due to competing design objectives, leading to underperforming operations and pitch constraint violations, especially under dynamic external conditions like wind speed and vibrations.
Innovation Solution
A consolidated multivariable controller implementing a multiple input/multiple output (MIMO) control approach with an optimal linear State Dependent Quadratic Regulator (SDQR) control unit, which computes online gain through real-time solution of a Discrete-time Algebraic Riccati Equation, and considers various operational and performance constraints, using either prestored linear model bank scheduling or online linearization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional SISO loops are used for wind turbine control, then the control architecture is simple and easy to implement, but control signal materialization cannot be guaranteed and pitch constraint violations occur
Solution Approach 1:
The patent merges multiple SISO controllers into a unified MIMO controller that coordinates all control inputs (blade pitch angles, generator torque, yaw angle) simultaneously. This consolidation ensures that control signals are materialized reliably by considering all actuator constraints and competing objectives in a single optimization framework, eliminating the guarantee problems of individual SISO loops.
Solution Approach 2:
The MIMO controller serves multiple functions simultaneously: it regulates power output, maintains rotational speed, reduces structural loads, and prevents actuator constraints violations. By integrating these previously separate control objectives into a single universal controller, the system ensures reliable control signal materialization across all functions while maintaining simplicity through unified optimization.
2Adaptability or versatility
If multiple SISO loops with multiple actuators are used, then various control objectives can be addressed, but competing design objectives lead to loss of control signal and underperforming operation
Solution Approach 1:
The patent combines multiple SISO controllers into a single MIMO controller that handles all control objectives (power regulation, speed control, load reduction) simultaneously through unified optimization. This merging eliminates the conflicting control signals that caused underperforming operations while maintaining the ability to address all control objectives through the coordinated actuation of multiple inputs.
3Reliability
If a consolidated MIMO controller with SDQR is implemented, then control signal materialization is ensured and performance is improved, but computational complexity increases due to real-time Riccati equation solution
Solution Approach 1:
The patent precomputes the Riccati equation solution offline to obtain the optimal gain matrix. This preliminary action eliminates the need for real-time Riccati equation solving during operation, reducing computational complexity while maintaining the reliability benefits of the MIMO SDQR controller. The precomputed gains are then applied in real-time based on current system states.
Solution Approach 2:
The patent implements gain scheduling that adapts the controller gains based on operating conditions such as wind speed and turbine power output. This dynamic adjustment allows the system to maintain optimal performance across varying conditions without requiring full real-time optimization, thereby reducing computational complexity while preserving control signal materialization guarantees.
4Speed
If prestored linear model bank scheduling is used, then real-time control is achieved with sufficient memory, but memory requirements increase significantly
Solution Approach 1:
The patent segments the operating space into discrete regions based on wind speed and power output ranges. Instead of storing a complete model bank for all possible conditions, the system uses a reduced set of linearized models corresponding to key operating points. This segmentation reduces memory requirements while maintaining real-time control capability through appropriate model selection based on current operating conditions.
Data Source
AI summary
A system for wind turbine control includes a state dependent quadratic regulator (SDQR) control unit, a linear quadratic regulator (LQR) generating control acceleration commands for wind turbine speed and wind turbine power regulation, an actuator dynamic model computing a gain value for the LQR at predetermined sampling intervals and augmenting the actuator dynamic model with a wind turbine model. The wind turbine model either an analytical linearization model or a precomputed linear model, where the precomputed linear model is selected from a model bank based on a real-time scheduling operation, and the analytical linearization model is computed using an online linearization operation in real-time at time intervals during operation of the wind turbine based on current wind turbine operating point values present at about the time of linearization. A method and a non-transitory medium are also disclosed.