Abstract
With the trend of increasing wind turbine rotor diameters, the mitigation of blade fatigue loadings is of special interest to extend the turbine lifetime. Fatigue load reductions can be partly accomplished using individual pitch control (IPC), and is commonly facilitated by the so-called multiblade coordinate (MBC) transformation. This operation transforms and decouples the blade load signals in a non-rotating yaw-axis and tilt-axis. However, in practical scenarios, the resulting transformed system still shows coupling between the axes. To cope with this phenomenon, earlier research has shown that the introduction of an additional MBC tuning variable – the azimuth offset – decouples the multivariable system. However, the introduction of this extra variable complicates the controller design process, and requires expert knowledge and specialized analysis software. To provide an efficient method for the optimization of fixed-structure IPC controllers, based on black box and computationally costly objective functions, this paper considers a Bayesian optimization controller tuning framework. Results show the efficiency of the framework to tune a combined 1P + 2P IPC implementation, without prior knowledge, and based on high-fidelity simulation results using a computationally expensive objective function.
Highlights
Wind turbine rotors are getting ever larger, which supports the sustained demand of increased wind turbine power ratings
One such control method is individual pitch control (IPC). This technique is applicable to more recent wind turbines, with their ability to pitch the blades to distinct angles, and to measure the blade root bending moments
The PI and expected improvement (EI) strategies are both part of improvement-based acquisition functions, and optimize for new point evaluations that have a likely probability of improvement
Summary
Wind turbine rotors are getting ever larger, which supports the sustained demand of increased wind turbine power ratings. The work in [10] shows that determination of the optimal offset value requires expert control knowledge and analysis software For this reason, an automated controller tuning framework, by minimization of an optimization objective, forms an interesting opportunity for efficient parameter tuning. The fixed-frame tilt and yaw pitch angles are formed by the IPC controller, implemented in this paper as two decoupled SISO control loops. The non-rotating signals are converted to implementable IPC pitch contributions in the rotating frame by the reverse MBC transformation θ1,n(t) θ2,n(t) = T−n 1(ψ + ψon) θ3,n(t) θt,n(t) θy,n(t). Where θt and θy are respectively the fixed-frame tilt and yaw pitch signals, and ψon is the azimuth offset for each harmonic. The offset is used in this paper for further decoupling of the tilt and yaw axes enabling the implementation of SISO IPC control loops.
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