Model predictive and linear quadratic Gaussian control of a wind turbine

Hur, Sung-ho; Leithead, W.E.

doi:10.1002/oca.2244

Cited by 20 publications

(14 citation statements)

References 17 publications

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“…5) than OT (12. 4) (Hur and Leithead 2017). During the particular time range of (380-500 s) the wind speed by the TSR method gave less variation than MPPT method (Bianchi, de Battista, and Mantz 2006).…”

Section: Discussionmentioning

confidence: 94%

Extracting Maximum Power from Wind Turbine Using Tip Speed Ratio and PO Algorithms by Limiting the Wind Speed

Suja¹,

Yuvaraj²

2021

revistageintec

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Aim: This research is focused on obtaining the maximum power by controlling the speed of the wind turbine. Materials and methods: Innovative Tip speed ratio and PO algorithm are implemented for obtaining maximum power from the wind turbines. Result: By controlling the wind speed (1. 9087rad/sec) we obtained maximum power output (200kW) from the wind turbine to increase its efficiency level. Conclusion: Tip speed ratio method (200kW) provides better power output compared to PO algorithm (192kW) for the selected data set.

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Section: Discussionmentioning

confidence: 94%

Extracting Maximum Power from Wind Turbine Using Tip Speed Ratio and PO Algorithms by Limiting the Wind Speed

Suja¹,

Yuvaraj²

2021

revistageintec

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“…Now, we have verified (15) for k = N. Assume that k−1 are as (11) for all k ≥ n + 1. Then we will show that (11) also holds for k = n. Set u k−d to be optimal for all k ≥ n + 1.…”

Section: Problem Formulationmentioning

confidence: 95%

“…In addition, the time delay d is 5, and the cost function (2) with N = 50, P N+1 = 0 with initial values x 0 = 0.9, u −5 = −0.72, u −4 = −1.04, u −3 = 1.36, u −2 = −0.31, u −1 = 0.64. By applying Theorem 3 and (46) to (48), (10), (11), direct calculation yields B k , Ω k , N k , T k with k = N, … , 1. We can obviously know that Ω i is invertible for i = 50, … , 5; there is a unique solution to the NCSs (48) according to Theorem 3.…”

Section: Numerical Examplesmentioning

confidence: 99%

Optimal linear quadratic Gaussian control for discrete time‐varying system with simultaneous input delay and state/control‐dependent noises

Zhang

Liang

et al. 2020

Optim Control Appl Methods

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This article focuses on the problem of linear quadratic Gaussian (LQG) control for discrete time-varying system with input delay and state/control-dependent noises. When the state variables can be exactly observed, first, we obtain the maximum principle by applying the method of variation. Second, a nonhomogeneous relationship between the state and the costate is developed in virtue of the obtained maximum principle and the mathematical induction. It is noted that the nonhomogeneous relationship is the solution to the forward and backward stochastic difference equations (FBSDEs). Finally, based on the solution to the FBSDEs, a necessary and sufficient condition for the optimal LQG control is derived in terms of coupled Riccati equations. Moreover, the analytical expression of the optimal controller is presented. The derived results are applied in networked control systems with packet dropout. Numerical examples are shown to illustrate the effectiveness of the proposed algorithm. K E Y W O R D SLQG control, discrete time-varying system, input delay, state/control-dependent noises INTRODUCTIONThe optimal linear quadratic Gaussian (LQG) control problem has received considerable attentions. 1-4 Generally, the standard LQG model contains the additive Gaussian white noises that are not state/control dependent. 5,6 With the rapid development of networked control systems (NCSs), the optimal control has been applied in various fields, such as vehicle industry, building automation surveillance, and Internet-based teleoperation, 7-11 which makes the control system models be more complicated. Obviously, the standard LQG model has no longer been applicable to the practical applications. Hence, we focus on the study of the LQG control systems with both state/control-dependent noises and input delay. It is generally known that the packet dropout occurred in NCSs can be described by the state/control-dependent noises. On one hand, many references have been investigated for LQG control with state/control-dependent noises. [12][13][14][15][16][17][18] Joshi 12 presented a suboptimal controller by using the "enforced separation principle." For NCSs with any arbitrary packet-drop pattern, Gupta et al 13 investigated the optimal LQG control by decomposing the problem into a standard LQR state-feedback controller, along with an optimal encoder-decoder design. Sinopoli et al 14 modeled the arrival of both observations and control packets as stochastic Bernoulli processes to describe the packet dropout. Liang and Xu 15 studied 882

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“…System predictions are obtained using a mathematical model of the system as well measurements of the outputs at each sample. Many studies have adopted MPC design in wind turbines, and their results demonstrated the effectiveness of the MPC for handling constraints on the rotor speed and blade‐pitch actuators. In addition, apart from the constraint handling feature, MPC can also incorporate preview information into the control design systematically.…”

Section: Introductionmentioning

confidence: 99%

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

2017

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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.

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Model predictive and linear quadratic Gaussian control of a wind turbine

Cited by 20 publications

References 17 publications

Extracting Maximum Power from Wind Turbine Using Tip Speed Ratio and PO Algorithms by Limiting the Wind Speed

Extracting Maximum Power from Wind Turbine Using Tip Speed Ratio and PO Algorithms by Limiting the Wind Speed

Optimal linear quadratic Gaussian control for discrete time‐varying system with simultaneous input delay and state/control‐dependent noises

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

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