Fundamental performance similarities between individual pitch control strategies for wind turbines

Lio, Wai Hou; Jones, Bernard J. T.; Lu, Qian; Rossiter, J.A.

doi:10.1080/00207179.2015.1078912

Cited by 30 publications

(32 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We begin by considering the single‐blade formulation of an IPC controller, shown in Figure . The single‐blade formulation is chosen for simplicity, but the results in this paper extend to fixed frame of reference IPCs, owing to the performance equivalence results established in Lio et al The single‐blade model is represented by a transfer function

G false(s false) \in scriptR

, where s is a complex variable, and

scriptR

is the space of real‐rational transfer functions with 1 input and 1 output. This transfer function relates the IPC blade pitch‐angle input u IPC to the measurement

y_{\overset{M}{true}}

of the perturbation flap‐wise bending moments

\overset{M}{true}

at the blade root.…”

Section: Performance Limitation Of Existing Ipc Architecturesmentioning

confidence: 99%

Overcoming fundamental limitations of wind turbine individual blade pitch control with inflow sensors

2018

View full text Add to dashboard Cite

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.

show abstract

G false(s false) \in scriptR

, where s is a complex variable, and

scriptR

is the space of real‐rational transfer functions with 1 input and 1 output. This transfer function relates the IPC blade pitch‐angle input u IPC to the measurement

y_{\overset{M}{true}}

of the perturbation flap‐wise bending moments

\overset{M}{true}

at the blade root.…”

Section: Performance Limitation Of Existing Ipc Architecturesmentioning

confidence: 99%

Overcoming fundamental limitations of wind turbine individual blade pitch control with inflow sensors

2018

View full text Add to dashboard Cite

show abstract

“…The transform‐based IPC techniques involve coordinate mappings on the pitch inputs, which complicate the constraint formulation in MPC, where the constraint inequalities need to be updated online at every sample, on the basis of the prediction of azimuth angle. In addition, as proved in the study of Lio et al ., the performance differences between the various types of IPCs are negligible. Consequently, single‐blade control IPC is employed in this work, where each blade is equipped with its own controller ( K θ M ) in response to a local blade load measurement.…”

Section: Wind Turbine Modelling and Nominal Robust Feedback Compensatormentioning

confidence: 99%

“…The simulation results in the study of Lio et al . showed that a controller of the form could be designed to be insensitive to such coupling by shaping the open‐loop frequency response to have low gain at the tower frequency. Similar to the plant model, the feedback controller has a discrete‐time state‐space realization:

\begin{matrix} right x_{κ} (k + 1) & left = A_{κ} x_{κ} (k) - B_{κ} y (k), & right \\ right u (k) & left = C_{κ} x_{κ} (k) - D_{κ} y (k), \end{matrix}

where the state vector

x_{κ} \in {double-struckR}^{n_{x_{κ}}}

is a collection of variables that characterizes the dynamics of the controller K and the subscript κ denotes controller.…”

Section: Wind Turbine Modelling and Nominal Robust Feedback Compensatormentioning

confidence: 99%

Preview predictive control layer design based upon known wind turbine blade‐pitch controllers

2017

View full text Add to dashboard Cite

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.

show abstract

“…Nonetheless, the Coleman transform-based IPC typically targets the static and 3p (thrice per revolution) non-rotating loads caused by the blade (e.g. 0 and 0.6 Hz) [20], whilst tower loads occur mainly at the tower resonant frequency (0.32Hz). Therefore, with a view towards avoiding the undesired couplings, the tower controller is designed as an inverse notch filter with gain concentrated at the tower resonant frequency, away from multiples of the blade rotational frequency:…”

Section: B Estimation-based Controller Designmentioning

confidence: 99%

“…T ∈ R n d are the referred measurements of the flap-wise blade moments, pitch angle signals and wind speeds upon the fixed reference frame, whilst ξ(t) ∈ R n ξ is the projection of the states associated with the blade dynamics upon a non-rotating reference frame (19) and the states of the tower dynamics (20).…”

Section: Transformation To An Lti System and Observability Analysismentioning

confidence: 99%

Estimation and Control of Wind Turbine Tower Vibrations Based on Individual Blade-Pitch Strategies

Lio

Jones

Rossiter

2019

IEEE Trans. Contr. Syst. Technol.

Self Cite

View full text Add to dashboard Cite

Abstract-In this paper, we present a method to estimate the tower fore-aft velocity based upon measurements from blade load sensors. In addition, a tower dampening control strategy is proposed, based upon an individual blade pitch control architecture that employs this estimate. The observer design presented in this paper exploits the Coleman transformations that convert a time-varying turbine model into one that is linear and time-invariant, greatly simplifying the observability analysis and subsequent observer design. The proposed individual pitch-based tower controller is decoupled from the rotor speed regulation loop and hence does not interfere with the nominal turbine power regulation. Closed-loop results, obtained from high fidelity turbine simulations, show close agreement between the tower estimates and the actual tower velocity. Furthermore, the individual-pitchbased tower controller achieves similar performance compared to the collective-pitch-based approach but with negligible impact upon the nominal turbine power output.Index Terms-State estimation of dynamical systems, Kalman filter, active damping control, wind energy.

show abstract

Fundamental performance similarities between individual pitch control strategies for wind turbines

Cited by 30 publications

References 18 publications

Overcoming fundamental limitations of wind turbine individual blade pitch control with inflow sensors

Overcoming fundamental limitations of wind turbine individual blade pitch control with inflow sensors

Preview predictive control layer design based upon known wind turbine blade‐pitch controllers

Estimation and Control of Wind Turbine Tower Vibrations Based on Individual Blade-Pitch Strategies

Contact Info

Product

Resources

About