Wind Turbine Predictive Control With Aeroelastic Load Constraints
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Solution Overview
Problem
Existing wind turbine control methods using Model Predictive Control (MPC) struggle with accurately handling loads on turbine components due to simplified load calculations, leading to potential safety issues and overly conservative designs.
Innovation Solution
A control method that utilizes a predictive aeroelastic model to determine primary loads and a strength calculation module to calculate secondary load parameters, optimizing a cost function subject to these constraints to improve load handling and avoid exceeding operational limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If simplified load calculations are used in MPC control, then computational complexity is reduced and control speed is improved, but load handling accuracy deteriorates leading to potential safety issues and overly conservative designs
Solution Approach 1:
The load calculation is divided into two segments: primary loads calculated by the predictive aeroelastic model for real-time control, and secondary load parameters calculated by the strength calculation module for accuracy. This segmentation allows the system to maintain fast control response while achieving accurate load assessment through coordinated computation of different load components.
Solution Approach 2:
The strength calculation module pre-calculates secondary load parameters based on primary loads before they are needed for control decisions. By performing this detailed strength assessment in advance and integrating it with the MPC framework, the system prepares accurate load information without delaying the control response when it is critically needed.
2Measurement precision
If detailed strength calculations are performed for all components, then load handling accuracy is improved, but computational power requirements increase making real-time control impractical
Solution Approach 1:
The calculation system is segmented into two modules with different computational demands: the predictive aeroelastic model handles real-time primary load calculations with lower computational requirements, while the strength calculation module performs detailed secondary parameter calculations. This segmentation distributes computational tasks appropriately, enabling accurate load assessment without overwhelming computational power requirements.
Solution Approach 2:
Instead of performing exhaustive strength calculations for all possible load cases, the system calculates secondary load parameters selectively based on the primary loads from the predictive model. This partial action approach focuses computational resources on the most relevant load parameters needed for real-time control decisions, avoiding unnecessary excessive calculations.
3Device complexity
If simplified load assessment is used, then computational requirements are reduced, but safety margins must be increased to account for uncertainties in load calculations
Solution Approach 1:
The strength calculation module provides feedback on secondary load parameters that refine the accuracy of load assessment. This feedback loop allows the system to identify when detailed strength calculations indicate lower actual loads than simplified models predict, enabling reduction of safety margins while maintaining reliability. The feedback mechanism continuously validates load predictions against more accurate strength-based calculations.
Solution Approach 2:
The system performs preliminary strength calculations to establish accurate baseline load parameters before final control decisions are made. By preparing this accurate load information in advance through the strength calculation module, the system reduces uncertainty in load assessment, which directly enables smaller safety margins while maintaining the same level of reliability.
4Reliability
If conservative safety margins are applied to account for calculation uncertainties, then reliability is improved, but energy capture efficiency deteriorates due to overly restrictive control
Solution Approach 1:
The feedback from the strength calculation module provides accurate information about actual load levels, enabling the control system to distinguish between situations where conservative margins are truly needed and where they are unnecessarily restrictive. This feedback-driven approach adjusts safety margins dynamically based on real load conditions, maintaining reliability while avoiding excessive conservatism that would limit energy capture.
Solution Approach 2:
The system transitions from static conservative safety margins to dynamic safety margins that adapt based on real-time load assessments from both the predictive aeroelastic model and the strength calculation module. This dynamic adjustment allows the control system to be restrictive only when actually necessary for safety, while permitting more aggressive control actions when accurate calculations show loads are well within limits, thereby improving energy capture efficiency.
Data Source
AI summary
A method for controlling a wind turbine having a plurality of actuators includes receiving operational data of the wind turbine and determining an operational state thereof. The method also includes using a control model to predict potential operational states depending on operation of the actuators over a finite period of time. The control model includes an aeroelastic model to determine loads based on the operational data. The control model further includes a strength calculation module to calculate secondary load parameters from the loads, constraints being defined for the secondary load parameters. The method further includes optimizing a cost function over an optimization period of time, subject to the constraints, to determine an optimum trajectory comprising commands for the actuators. The method further includes using the first commands of the optimum trajectory to control the actuators.