ACC19refs.bib

@article{Ossmann2017,
author = {Ossmann, Daniel and Theis, Julian and Seiler, Peter},
doi = {10.1002/we.2121},
file = {:H$\backslash$:/My Drive/{\_}PhD work/Resources {\&} references/11xx{\_}WT control/1141{\_}Ossmann et al{\_}2017.pdf:pdf},
journal = {Wind Energy},
keywords = {load reduction,robust control,wind turbine control},
month = {Jun.},
pages = {1771--1786},
title = {{Load reduction on a Clipper Liberty wind turbine with linear parameter-varying individual blade pitch control}},
volume = {20},
year = {2017}
}
@inproceedings{Schlipf_lidar2015,
address = {Paris, France},
author = {Schlipf, David and Fleming, Paul and Raach, Steffen and Scholbrock, Andrew and Haizmann, Florian and Krishnamurthy, Raghu and Boquet, Matthieu and Cheng, Po Wen},
booktitle = {EWEA Annual Event},
file = {:H$\backslash$:/My Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1122{\_}Schlipf et al{\_}2015.pdf:pdf},
keywords = {December 2015,NREL,NREL/CP-5000-65273,data processing,feedforward lidar,lidar-assisted,wind turbine controls},
month = {Dec.},
title = {{An Adaptive Data Processing Technique for Lidar-Assisted Control to Bridge the Gap Between Lidar Systems for Wind Turbines}},
year = {2015}
}
@inproceedings{SimleyPao_roteff2013,
address = {Washington, D.C.},
author = {Simley, Eric and Pao, Lucy},
booktitle = {Proc. American Control Conference},
file = {:H$\backslash$:/My Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1101{\_}Simley {\&} Pao{\_}2013.pdf:pdf},
isbn = {9781479901784},
pages = {621--627},
month = {Jun.},
title = {{Reducing LIDAR Wind Speed Measurement Error with Optimal Filtering}},
year = {2013}
}
@inproceedings{Sinner2018,
address = {Milwaukee, WI},
author = {Sinner, Michael and Pao, Lucy},
booktitle = {Proc. American Control Conference},
file = {:H$\backslash$:/My Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1112{\_}Sinner {\&} Pao{\_}2018.pdf:pdf},
isbn = {9781538654286},
month = {Jun.},
pages = {1509--1514},
title = {{A Comparison of Individual and Collective Pitch Model Predictive Controllers for Wind Turbines}},
year = {2018}
}
@inproceedings{Bir2008,
abstract = {The dynamics of wind turbine rotor blades are generally expressed in rotating frames attached to the individual blades. The rotor, however, responds as a whole to excitations such as aerodynamic gusts, control inputs, and tower-nacelle motionall of which occur in a nonrotating frame. Similarly, the tower-nacelle subsystem sees the combined effect of all rotor blades, not the individual blades. Multi-blade coordinate transformation (MBC) helps integrate the dynamics of individual blades and express them in a fixed (nonrotating) frame. MBC involves two steps: transformation of the rotating degrees of freedom, and transformation of the equations of motion. This paper details the MBC operation. A new MBC scheme is developed that is applicable to variable-speed turbines, which may also have dissimilar blades. The scheme also covers control, disturbance, output, and feed-forward matrices. Depending on the analysis objective, wind turbine researchers may generate system matrices either in the first-order (state-space) form or the second-order (physical-domain) form. We develop MBC relations for both these forms. MBC is particularly essential for modal and stability analyses. Commonly, wind turbine researchers first compute the periodic state-space matrix, time-average it over the rotor rotational period, and then apply conventional eigenanalysis to compute modal and stability characteristics. Direct averaging, however, eliminates all periodic terms that contribute to system dynamics, thereby producing errors. While averaging itself is not always a bad approach, it must follow MBC. Sample results are presented to illustrate this point and also to show the application of MBC to the modal and stability analysis of a 5-MW turbine.},
author = {Bir, Gunjit},
booktitle = {ASME Wind Energy Symposium},
doi = {10.2514/6.2008-1300},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/MBC{\_}GBir.pdf:pdf},
isbn = {9781563479373},
keywords = {Bir2008,January 2008,NREL/CP-500-42553,wind turbine,wind turbine blades,wind turbine computer modeling},
mendeley-tags = {Bir2008},
number = {},
pages = {1--15},
title = {{Multi-blade Coordinate Transformation and its Application to Wind Turbine Analysis}},
year = {Jan. 2008}
}
@inproceedings{Bobanac2015,
author = {Bobanac, V and Vasak, M},
booktitle = {IEEE International Conference on Industrial Technology},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/ACC intro/1142{\_}Bobanac {\&} Vasak{\_}2015.pdf:pdf},
pages = {85--92},
title = {{Adaptive {$H_\infty$} Control of Large Wind Turbines}},
year = {2015}
}
@article{Bossanyi2003,
abstract = {Ifapitch-regulatedwindturbinehasindividual pitchactuators for eachblade, thepossibility arises to send different pitch angle demands to each blade. The possibility of using this as a wayof reducing loads has been suggestedmanytimes over the years, but the idea has yet to gain full commercial acceptance. There are a number of reasons why this situation may be set to change, and very significant load reductions can result. Copyright{\textcopyright}2002},
author = {Bossanyi, E. A.},
doi = {10.1002/we.76},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/1171{\_}Bossanyi{\_}2003{\_}IPC.pdf:pdf},
issn = {10954244},
journal = {Wind Energy},
keywords = {Fatigue,Individual pitch control,Load reduction,Loads,Turbulence,Wind turbine},
number = {2},
pages = {119--128},
title = {{Individual Blade Pitch Control for Load Reduction}},
volume = {6},
year = {2003}
}
@article{Bottasso2014,
author = {Bottasso, C L and Pizzinelli, P and Riboldi, C E F and Tasca, L},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1111{\_}Bottasso et al{\_}2014.pdf:pdf},
journal = {Renewable Energy},
month = {Nov.},
pages = {442--452},
title = {{LiDAR-Enabled Model Predictive Control of Wind Turbines with Real-Time Capabilities}},
volume = {71},
year = {2014}
}
@inproceedings{Chen2014,
author = {Chen, Z. J. and Stol, K. A.},
booktitle = {Journal of Physics: Conference Series},
doi = {10.1088/1742-6596/524/1/012045},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/Chen{\_}2014{\_}J.{\_}Phys.{\%}3A{\_}Conf.{\_}Ser.{\_}524{\_}012045.pdf:pdf},
isbn = {1742-6588 *****************},
issn = {17426596},
number = {1},
title = {{An Assessment of the Effectiveness of Individual Pitch Control on Upscaled Wind Turbines}},
volume = {524},
year = {2014}
}
@techreport{Hayman2012,
abstract = {MLife is a MATLAB-based tool created to post-process results from wind turbine tests, and aero-elastic, dynamic simulations. MLife computes statistical information and fatigue estimates for one or more time-series. We specifically designed MLife to handle hundreds of time-series. The program reads a text-based settings file in conjunction with one or more time-series data files. Alternatively, the program can read parameter variables which were created using MATLAB, outside of MLife. The program generates results in the form of MATLAB variables, text output files, and/or Excel formatted files. This allows you to make other calculations or present the data in ways MLife cannot. The fatigue calculations include short-term damage-equivalent loads (DELs) and damage rates, which are based on single time-series, lifetime DEL results based on the entire set of time-series data, and the accumulated lifetime damage and the time until failure.},
address = {Golden, CO},
author = {Hayman, G J},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/User guides/MLife{\_}Theory.pdf:pdf},
institution = {NREL},
number = {},
pages = {12},
title = {{MLife Theory Manual for Version 1.00}},
year = {Oct. 2012}
}
@inproceedings{Gros2013_2,
address = {Florence, Italy},
author = {Gros, S and Vukov, M and Diehl, M},
booktitle = {IEEE Conference on Decision and Control},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1112{\_}Gros et al{\_}2013.pdf:pdf},
month = {Dec.},
pages = {1007--1012},
title = {{A Real-time MHE and NMPC Scheme for Wind Turbine Control}},
year = {2013}
}
@article{Gros2017,
author = {Gros, Sebastien and Schild, Axel},
doi = {10.1080/00207179.2016.1266514},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/Real time economic nonlinear model predictive control for wind turbine control.pdf:pdf},
issn = {13665820},
journal = {International Journal of Control},
keywords = {Economic NMPC,NMPC,Wind turbine control,real-time},
number = {12},
pages = {2799--2812},
publisher = {Taylor {\&} Francis},
title = {{Real-time Economic Nonlinear Model Predictive Control for Wind Turbine Control}},
volume = {90},
year = {2017}
}
@techreport{Jonkman2009,
abstract = {The TurbSim},
address = {Golden, CO},
author = {Jonkman, Bonnie},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/User guides/TurboSim guide.pdf:pdf},
institution = {NREL},
keywords = {JonkmanB2009,NREL/TP-500-46198,September 2009,TurbSim,inflow turbulence code,wind turbine design code simulations},
mendeley-tags = {JonkmanB2009},
number = {},
title = {{TurbSim User's Guide: Version 1.50}},
year = {Sep. 2009}
}
@techreport{Jonkman2005,
address = {Golden, CO},
archivePrefix = {arXiv},
arxivId = {ArXiv ID},
author = {Jonkman, Jason M and {Buhl Jr.}, Marshall L},
booktitle = {NREL/EL-500-38230},
doi = {10.2172/15020796},
eprint = {ArXiv ID},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/User guides/FASTv7{\_}User guide.pdf:pdf},
institution = {NREL},
isbn = {NREL/EL-500-38230},
issn = {1600-0447},
pmid = {21564034},
title = {{FAST User's Guide}},
year = {Aug. 2005}
}
@article{Koerber2013,
author = {Koerber, A and King, R},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1111{\_}Koerber {\&} King{\_}2013.pdf:pdf},
journal = {IEEE Transactions on Control Systems Technology},
month = {Jul.},
number = {4},
pages = {1117--1128},
title = {{Combined Feedback-Feedforward Control of Wind Turbines Using State-Constrained Model Predictive Control}},
volume = {21},
year = {2013}
}
@phdthesis{Laks2012,
address = {Boulder, CO},
author = {Laks, J H},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1114{\_}Laks{\_}2012.pdf:pdf},
school = {University of Colorado},
title = {{Preview Scheduled Model Predictive Control for Horizontal Axis Wind Turbines}},
year = {2012}
}
@techreport{Lazard2016,
abstract = {Lazard's Levelized Cost of Energy Analysis (" LCOE ") addresses the following topics:  Comparative " levelized cost of energy " analysis for various technologies on a {\$}/MWh basis, including sensitivities, as relevant, for U.S. federal tax subsidies, fuel costs, geography and cost of capital, among other factors  Comparison of the implied cost of carbon abatement for various generation technologies  Illustration of how the cost of various generation technologies compares against illustrative generation rates in a subset of the largest metropolitan areas of the U.S.  Illustration of utility-scale and rooftop solar versus peaking generation technologies globally  Illustration of how the costs of utility-scale and rooftop solar and wind vary across the U.S., based on illustrative regional resources  Illustration of the declines in the levelized cost of energy for various generation technologies over the past several years  Comparison of assumed capital costs on a {\$}/kW basis for various generation technologies  Illustration of the impact of cost of capital on the levelized cost of energy for selected generation technologies  Decomposition of the levelized cost of energy for various generation technologies by capital cost, fixed operations and maintenance expense, variable operations and maintenance expense, and fuel cost, as relevant  Considerations regarding the usage characteristics and applicability of various generation technologies, taking into account factors such as location requirements/constraints, dispatch capability, land and water requirements and other contingencies  Summary assumptions for the various generation technologies examined  Summary of Lazard's approach to comparing the levelized cost of energy for various conventional and Alternative Energy generation technologies Other factors would also have a potentially significant effect on the results contained herein, but have not been examined in the scope of this current analysis. These additional factors, among others, could include: capacity value vs. energy value; stranded costs related to distributed generation or otherwise; network upgrade, transmission or congestion costs or other integration-related costs; significant permitting or other development costs, unless otherwise noted; and costs of complying with various environmental regulations (e.g., carbon emissions offsets, emissions control systems). The analysis also does not address potential social and environmental externalities, including, for example, the social costs and rate consequences for those who cannot afford distribution generation solutions, as well as the long-term residual and societal consequences of various conventional generation technologies that are difficult to measure (e.g., nuclear waste disposal, environmental impacts, etc.) While prior versions of this study have presented the LCOE inclusive of the U.S. Federal Investment Tax Credit and Production Tax Credit, Versions 6.0 – 10.0 present the LCOE on an unsubsidized basis, except as noted on the page titled " Levelized Cost of Energy—Sensitivity to U.S. Federal Tax Subsidies " Introduction},
author = {Lazard},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/ACC intro/levelized-cost-of-energy-v100.pdf:pdf},
keywords = {LazardLCOE2016},
mendeley-tags = {LazardLCOE2016},
number = {},
pages = {1--21},
title = {{Lazard's Levelised Cost of Energy Analysis (version 10.0)}},
year = {Dec. 2016}
}
@inproceedings{Mirzaei2016,
address = {Boston, MA},
author = {Mirzaei, M and Hansen, M H},
booktitle = {Proc. American Control Conference},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1112{\_}Mirzaei {\&} Hansen{\_}2016.pdf:pdf},
month = {Jul.},
pages = {1381--1386},
title = {{A LIDAR-assisted Model Predictive Controller Added on a Traditional Wind Turbine Controller}},
year = {2016}
}
@inproceedings{Mirzaei2012,
address = {Dubrovnik, Croatia},
author = {Mirzaei, M and Henriksen, L C and Poulsen, N K and Niemann, H H and Hansen, M H},
booktitle = {IEEE International Conference on Control Applications},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1112{\_}Mirzaei et al{\_}2012{\_}2.pdf:pdf},
month = {Oct.},
pages = {1646--1651},
title = {{Individual Pitch Control Using LIDAR Measurements}},
year = {2012}
}
@article{Ostergaard2009,
abstract = {This paper considers the design of linear parameter varying (LPV) controllers for wind turbines in order to obtain a multivariable control law that covers the entire nominal operating trajectory. The paper first presents a controller structure for selecting a proper operating trajectory as a function of estimated wind speed. The dynamic control law is based on LPV controller synthesis with general parameter dependency by gridding the parameter space. The controller construction can, for medium- to large-scale systems, be difficult from a numerical point of view, because the involved matrix operations tend to be ill-conditioned. The paper proposes a controller construction algorithm together with various remedies for improving the numerical conditioning the algorithm. The proposed algorithm is applied to the design of a LPV controller for wind turbines, and a comparison is made with a controller designed using classical techniques to conclude that an improvement in performance is obtained for the entire operating envelope.},
author = {{\O}stergaard, Kasper Zinck and Stoustrup, Jakob and Brath, Per},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/ACC intro/11x1{\_}Ostergaard et al{\_}2008{\_}LPV.pdf:pdf},
journal = {International Journal of Robust and Nonlinear Control},
keywords = {control of wind turbines,gain scheduling,linear parameter varying control,numerical conditioning},
number = {1},
pages = {92--116},
title = {{Linear Parameter Varying Control of Wind Turbines Covering both Partial Load and Full Load Conditions}},
volume = {19},
year = {2009}
}
@article{Pao2011CSM,
author = {Pao, Lucy Y. and Johnson, Kathryn E.},
doi = {10.1109/MCS.2010.939938},
journal = {IEEE Control Systems Magazine},
month = {Apr.},
pages = {44--62},
title = {{Control of Wind Turbines}},
year = {2011}
}
@inproceedings{Raach2014,
address = {Portland, OR},
author = {Raach, S and Schlipf, D and Sandner, F and Matha, D and Cheng, P W},
booktitle = {Proc. American Control Conference},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1112{\_}Raach et al{\_}2014.pdf:pdf},
month = {Jun.},
pages = {4434--4439},
title = {{Nonlinear Model Predictive Control of Floating Wind Turbines with Individual Pitch Control}},
year = {2014}
}
@article{Rawlings2000,
abstract = {The paper provides a reasonably accessible and self-contained tutorial exposition on model predictive control (MPC). It is aimed at readers with control expertise, particularly practitioners, who wish to broaden their perspective in the MPC area of control technology. We introduce the concepts, provide a framework in which the critical issues can be expressed and analyzed, and point out how MPC allows practitioners to address the trade-offs that must be considered in implementing a control technology...},
author = {Rawlings, James B.},
doi = {10.1109/37.845037},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/101x{\_}MPC general/101{\_}Rawlings{\_}2000.pdf:pdf},
isbn = {0272-1708},
issn = {1066033X},
journal = {IEEE Control Systems Magazine},
number = {3},
pages = {38--52},
title = {{Tutorial Overview of Model Predictive Control}},
volume = {20},
year = {2000}
}
@inproceedings{Schlipf2014_2,
address = {Portland, OR},
author = {Schlipf, D and Grau, P and Raach, S and Duraiski, R and Trierweiler, J and Cheng, P W},
booktitle = {Proc. American Control Conference},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1112{\_}Schlipf et al{\_}2014.pdf:pdf},
month = {Jun.},
pages = {3742--3747},
title = {{Comparison of Linear and Nonlinear Model Predictive Control of Wind Turbines using LIDAR}},
year = {2014}
}
@article{Schlipf2014,
abstract = {This work presents the results from a field test of LIDAR assisted collective pitch control using a scanning LIDAR device installed on the nacelle of a mid-scale research turbine. A nonlinear feedforward controller is extended by an adaptive filter to remove all uncorrelated frequencies of the wind speed measurement to avoid unnecessary control action. Positive effects on the rotor speed regulation as well as on tower, blade and shaft loads have been observed in the case that the previous measured correlation and timing between the wind preview and the turbine reaction are accomplish. The feedforward controller had negative impact, when the LIDAR measurement was disturbed by obstacles in front of the turbine. This work proves, that LIDAR is valuable tool for wind turbine control not only in simulations but also under real conditions. Furthermore, the paper shows that further understanding of the relationship between the wind measurement and the turbine reaction is crucial to improve LIDAR assisted control of wind turbines.},
author = {Schlipf, David and Fleming, Paul A. and Haizmann, Florian and Scholbrock, Andrew and Hofs{\"{a}}{\ss}, Martin and Wright, Alan and Cheng, Po Wen},
doi = {10.1088/1742-6596/555/1/012090},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/ACC intro/1121{\_}Schlipf et al{\_}2012{\_}FF.pdf:pdf},
issn = {1742-6588},
journal = {Journal of Physics: Conference Series},
keywords = {NREL/JA-5000-55222},
number = {1},
pages = {},
title = {{Field Testing of Feedforward Collective Pitch Control on the CART2 Using a Nacelle-Based Lidar Scanner}},
volume = {555},
year = {2014}
}
@inproceedings{SimleyPao2015,
address = {Chicago, IL},
author = {Simley, E and Pao, L Y},
booktitle = {Proc. American Control Conference},
month = {Jul.},
pages = {3708--3714},
title = {{A Longitudinal Spatial Coherence Model for Wind Evolution based on Large-eddy Simulation}},
year = {2015}
}
@inproceedings{Soltani2011,
address = {Denver, CO},
author = {Soltani, M and Wisniewski, R and Brath, P and Boyd, S},
booktitle = {IEEE International Conference on Control Applications},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1112{\_}Soltani et al{\_}2011.pdf:pdf},
month = {Sep.},
pages = {852--857},
title = {{Load Reduction of Wind Turbines Using Receding Horizon Control}},
year = {2011}
}
@techreport{Wiser2017,
address = {Berkeley, CA},
author = {Wiser, Ryan and Bolinger, Mark},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/ACC intro/2016{\_}Wind{\_}Technologies{\_}Market{\_}Report{\_}0.pdf:pdf},
institution = {U.S. DOE, LBNL},
keywords = {WindTechMrktRep2016},
mendeley-tags = {WindTechMrktRep2016},
title = {{Wind Technologies Market Report 2016}},
year = {2017}
}
@article{Yang2015,
abstract = {In wind farm operation, the performance and loads of downstream turbines are heavily influenced by the wake of the upstream turbines. Furthermore, the actual wake is more challenging due to the dynamic phenomenon of wake meandering, i.e. the turbine wake often demonstrates dynamic shift over time. To deal with the time-varying characteristics of wake meandering, a multiple model predictive control (MMPC) scheme is applied to the individual pitch control (IPC) based load reduction. The coherence function in the spectral method is used to generate the stochastic wind profile including wake meandering at upstream turbine, and a simplified wake meandering model is developed to emulate the trajectory of the wake center at downstream turbine. The Larsen wake model and Gaussian distribution of wake deficit are applied for composing wind profiles across the rotor of downstream turbines. A set of MMPC controllers are designed based on different linearized state-space models, and are applied in a smooth switching manner. Simulation results show significant reduction in the variation of both rotor speed and blade-root flapwise bending moment using the MMPC based IPC by including the wake meandering, as compared to a benchmark PI controller designed by NREL.},
author = {Yang, Zhongzhou and Li, Yaoyu and Seem, John E.},
doi = {10.1016/j.conengprac.2015.08.009},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1111{\_}Yang et al{\_}2015.pdf:pdf},
issn = {09670661},
journal = {Control Engineering Practice},
keywords = {Individual pitch control,Load reduction,Multi-model predictive control,Wake meandering,Wind turbine control},
pages = {37--45},
publisher = {Elsevier},
title = {{Multi-model Predictive Control for Wind Turbine Operation under Meandering Wake of Upstream Turbines}},
volume = {45},
year = {2015}
}
@inproceedings{Mirzaei2013,
address = {Washington, D.C.},
author = {Mirzaei, M and Soltani, M and Poulsen, N K and Niemann, H H},
booktitle = {Proc. American Control Conference},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1112{\_}Mirzaei et al{\_}2013.pdf:pdf},
month = {Jun.},
pages = {2235--2240},
title = {{Model Predictive Control of Wind Turbines using Uncertain LIDAR Measurements}},
year = {2013}
}
@article{Schlipf2013,
author = {Schlipf, D and Schlipf, D J and Kuhn, M},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1111{\_}Schlipf et al{\_}2013.pdf:pdf},
journal = {Wind Energy},
month = {Oct.},
number = {7},
pages = {1107--1129},
title = {{Nonlinear Model Predictive Control of Wind Turbines Using LIDAR}},
volume = {16},
year = {2013}
}
@inproceedings{Tofighi2015,
address = {Gold Coast, Australia},
author = {Tofighi, E and Faulwasser, T and Kellet, C M},
booktitle = {Proc. Australian Control Conference},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1112{\_}Tofighi et al{\_}2015.pdf:pdf},
month = {Nov.},
pages = {210--214},
title = {{Nonlinear Model Predictive Control Approach for Structural Load Mitigation of Wind Turbines in Presence of Wind Measurement Uncertainties}},
year = {2015}
}
@article{Lio2017,
abstract = {A simple model for including the influence of the atmospheric boundary layer in connection with large eddy simulations of wind turbine wakes is presented and validated by comparing computed results with measurements as well as with direct numerical simulations. The model is based on an immersed boundary type technique where volume forces are used to intro- duce wind shear and atmospheric turbulence. The application of the model for wake studies is demonstrated by combining it with the actuator line method, and predictions are compared with field measurements. Copyright {\textcopyright} 2013 JohnWiley {\&} Sons, Ltd.},
archivePrefix = {arXiv},
arxivId = {arXiv:1006.4405v1},
author = {Lio, W. H. and Jones, B. Ll and Rossiter, J. A.},
doi = {10.1002/we.2090},
eprint = {arXiv:1006.4405v1},
file = {:C$\backslash$:/Users/misi9170/Google Drive/{\_}PhD work/Resources {\&} references/111x{\_}MPC for WTs/1111{\_}Lio et al{\_}2017.pdf:pdf},
isbn = {9780791843796},
issn = {10991824},
journal = {Wind Energy},
keywords = {blade-pitch control,feed-forward control,model predictive control,preview control},
number = {7},
pages = {1207--1226},
pmid = {21790082},
title = {{Preview predictive control layer design based upon known wind turbine blade-pitch controllers}},
volume = {20},
year = {2017}
}
@book{Rossiter2018,
author = {Rossiter, J. A.},
edition = {2},
isbn = {9781351597166},
publisher = {Taylor {\&} Francis Group},
title = {{A First Course in Predictive Control}},
year = {2018}
}
@book{Seborg2011,
address = {Hoboken, NJ},
author = {Seborg, Dale E and Edgar, Thomas F and Mellichamp, Duncan A and Doyle, Francis J},
edition = {3},
isbn = {978-0-470-12867-1},
publisher = {John Wiley {\&} Sons, Ltd.},
title = {{Process Dynamics and Control}},
year = {2011}
}