Wind Turbine MPC With Staged Control Outputs to Cut Compute Load
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Solution Overview
Problem
Model predictive control (MPC) methods for wind turbines face high computational demands due to the need for frequent re-solving of optimization problems, especially when a high resolution of blade azimuthal angle is required, leading to short time stages and increased computational requirements.
Innovation Solution
The method involves predicting wind turbine behavior over a prediction horizon divided into initial and subsequent time stages, where only control outputs for the subsequent stages are implemented, with a penalty parameter constraining deviations from previous outputs to reduce computational load, allowing for a longer time to solve optimization problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model predictive control is used with high resolution blade azimuthal angle, then control precision is improved, but computational requirements increase
Solution Approach 1:
The prediction horizon is divided into multiple time stages, where only control outputs for subsequent stages (not the initial stage) are implemented. This segmentation allows the optimization problem to be solved over a longer effective horizon while reducing the frequency of full re-optimization, thereby lowering computational requirements while maintaining control precision through the multi-stage approach.
2Speed
If the first time stage length is reduced to allow frequent optimization updates, then control responsiveness is improved, but computational time pressure increases
Solution Approach 1:
Control outputs for subsequent time stages are determined in advance as part of the optimization solution, but only implemented after the initial stage completes. This preliminary determination allows the system to plan ahead over a longer horizon without requiring re-optimization at every short time stage, reducing computational time pressure while maintaining responsiveness through the pre-planned control sequence.
3Measurement precision
If optimization is re-solved frequently to maintain accuracy, then control accuracy is improved, but computational load increases
Solution Approach 1:
The implementation strategy dynamically adjusts which control outputs are applied based on the time stage: control outputs for subsequent stages are implemented only after their corresponding initial stage completes. This dynamic approach allows longer effective prediction horizons and reduced optimization frequency while maintaining accuracy through staged implementation, balancing computational load with control precision.
Data Source
AI summary
The invention provides a method for controlling a wind turbine. The method predicts behaviour of the wind turbine components for the time stages over a prediction horizon using a wind turbine model describing dynamics of the wind turbine, where the time stages include a first set of time stages from an initial time stage and a second set of time stages subsequent to the first set. The method determines control outputs, e.g. individual blade pitch, for time stages based on the predicted behaviour. The method then transmits a control signal to implement only the control outputs for each of the second set of time stages so as to control the wind turbine. Advantageously, the invention reduces both average and peak computational loads relative to standard predictive control algorithms.