The use of blade individual pitch control (IPC) offers a means of reducing the harmful turbine structural loads that arise from the uneven and unsteady forcing from the oncoming wind. In recent years two different and competing IPC techniques have emerged that are characterised by the specific loads that they are primarily designed to attenuate. In the first instance, methodologies such as single-blade control and Clarke Transform-based control have been developed to reduce the unsteady loads on the rotating blades, whilst tilt-yaw control and its many variants instead target load reductions in the non rotating turbine structures, such as the tower and main bearing. Given the seeming disparities between these controllers, the aim of this paper is to show the fundamental performance similarities that exist between them and hence unify research in this area. Specifically, we show that single-blade controllers are equivalent to a particular class of tilt-yaw controller, which itself is equivalent to Clarke Transform-based control. This means that three architecturally dissimilar IPC controllers exist that yield exactly the same performance in terms of load reductions on fixed and rotating turbine structures. We further demonstrate this outcome by presenting results obtained from high-fidelity closed-loop turbine simulations.

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ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. ABSTRACTThe use of upstream wind measurements has motivated the development of blade-pitch preview controllers to improve rotor speed tracking and structural load reduction beyond that achievable via conventional feedback control. Such preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, alter the closedloop dynamics of the existing blade-pitch feedback control system. This can result in a deterioration of the robustness properties and performance of the existing feedback control system. Furthermore, performance gains from utilising the upcoming real-time measurements cannot be easily distinguished from the feedback control, making it difficult to formulate a clear business case for the use of preview control. Therefore, the aim of this work is to formulate a modular MPC layer on top of a given output feedback blade-pitch controller, with a view to retaining the closed-loop robustness and frequency-domain performance of the latter. We derive a key result that proves that the proposed modular MPC layer handles real-time advance measurements and impacts the existing closed-loop system if and only if constraints are violated. The separate nature of the proposed controller structure enables clear and transparent quantification of the benefits gained by using preview control, beyond that of the underlying feedback controller. This is illustrated by results obtained from high-fidelity closed-loop turbine simulations, showing the performance comparison between a nominal feedback controller and an additional MPC-based preview controller. The proposed control scheme incorporating knowledge of the oncoming wind and constraints achieved significant 43% and 30% reductions in the rotor speed and flap-wise blade moment standard deviations, respectively. Additionally, the chance of constraint violations on the rotor speed decreased remarkably from 2.15% to 0.01%, compared to the nominal controller.

Individual pitch control (IPC) provides an important means of attenuating harmful fatigue and extreme loads upon the load bearing structures of a wind turbine. Conventional IPC architectures determine the additional pitch demand signals required for load mitigation in response to measurements of the flap‐wise blade‐root bending moments. However, the performance of such architectures is fundamentally limited by bandwidth constraints imposed by the blade dynamics. Seeking to overcome this problem, we present a simple solution based upon a local blade inflow measurement on each blade. Importantly, this extra measurement enables the implementation of an additional cascaded feedback controller that overcomes the existing IPC performance limitation and hence yields significantly improved load reductions. Numerical demonstration upon a high‐fidelity and nonlinear wind turbine model reveals (1) 60% reduction in the amplitude of the dominant 1P fatigue loads and (2) 59% reduction in the amplitude of extreme wind shear‐induced blade loads, compared with a conventional IPC controller with the same robust stability margin. This paper therefore represents a significant alternative to wind turbine IPC load mitigation as compared with light detection and ranging‐based feedforward control approaches.

Abstract-This paper aims to give an overview of the recent development and benefits of model predictive control in wind turbines and its future potential. For a modern large wind turbine, the main objective of control is to maximise the power production while maintaining the fatigue loads to be minimal. With such multiple objectives, a multivariable system and actuators constraints the popular PI controller may become ineffective or hard to tune whereas MPC provides a systematic approach for designing a multivariable controller incorporating the knowledge of actuator constraints. This paper reviews the wind turbine control problem and in particular gives a survey of the use of model predictive control on wind turbines.