Research article
14 Mar 2019
Research article | 14 Mar 2019
An active power control approach for wake-induced load alleviation in a fully developed wind farm boundary layer
Mehdi Vali et al.
Related authors
A control-oriented dynamic wind farm model: WFSim
Sjoerd Boersma, Bart Doekemeijer, Mehdi Vali, Johan Meyers, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 75–95, https://doi.org/10.5194/wes-3-75-2018,https://doi.org/10.5194/wes-3-75-2018, 2018
Short summary
System-level design studies for large rotors
Daniel S. Zalkind, Gavin K. Ananda, Mayank Chetan, Dana P. Martin, Christopher J. Bay, Kathryn E. Johnson, Eric Loth, D. Todd Griffith, Michael S. Selig, and Lucy Y. Pao
Wind Energ. Sci., 4, 595–618, https://doi.org/10.5194/wes-4-595-2019,https://doi.org/10.5194/wes-4-595-2019, 2019
Short summary
A control-oriented dynamic wind farm model: WFSim
Sjoerd Boersma, Bart Doekemeijer, Mehdi Vali, Johan Meyers, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 75–95, https://doi.org/10.5194/wes-3-75-2018,https://doi.org/10.5194/wes-3-75-2018, 2018
Short summary
Transient LES of an offshore wind turbine
Lukas Vollmer, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 2, 603–614, https://doi.org/10.5194/wes-2-603-2017,https://doi.org/10.5194/wes-2-603-2017, 2017
Short summary
An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects
Niko Mittelmeier, Julian Allin, Tomas Blodau, Davide Trabucchi, Gerald Steinfeld, Andreas Rott, and Martin Kühn
Wind Energ. Sci., 2, 477–490, https://doi.org/10.5194/wes-2-477-2017,https://doi.org/10.5194/wes-2-477-2017, 2017
Short summary
Demonstration and uncertainty analysis of synchronised scanning lidar measurements of 2-D velocity fields in a boundary-layer wind tunnel
Marijn Floris van Dooren, Filippo Campagnolo, Mikael Sjöholm, Nikolas Angelou, Torben Mikkelsen, and Martin Kühn
Wind Energ. Sci., 2, 329–341, https://doi.org/10.5194/wes-2-329-2017,https://doi.org/10.5194/wes-2-329-2017, 2017
Short summary
Related subject area
Wind direction estimation using SCADA data with consensus-based optimization
Jennifer Annoni, Christopher Bay, Kathryn Johnson, Emiliano Dall'Anese, Eliot Quon, Travis Kemper, and Paul Fleming
Wind Energ. Sci., 4, 355–368, https://doi.org/10.5194/wes-4-355-2019,https://doi.org/10.5194/wes-4-355-2019, 2019
Short summary
Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1
Paul Fleming, Jennifer King, Katherine Dykes, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, Hector Lopez, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019,https://doi.org/10.5194/wes-4-273-2019, 2019
Short summary
System identification, fuzzy control and simulation of a kite power system with fixed tether length
Tarek N. Dief, Uwe Fechner, Roland Schmehl, Shigeo Yoshida, Amr M. M. Ismaiel, and Amr M. Halawa
Wind Energ. Sci., 3, 275–291, https://doi.org/10.5194/wes-3-275-2018,https://doi.org/10.5194/wes-3-275-2018, 2018
A simulation study demonstrating the importance of large-scale trailing vortices in wake steering
Paul Fleming, Jennifer Annoni, Matthew Churchfield, Luis A. Martinez-Tossas, Kenny Gruchalla, Michael Lawson, and Patrick Moriarty
Wind Energ. Sci., 3, 243–255, https://doi.org/10.5194/wes-3-243-2018,https://doi.org/10.5194/wes-3-243-2018, 2018
Short summary
Field test of wake steering at an offshore wind farm
Paul Fleming, Jennifer Annoni, Jigar J. Shah, Linpeng Wang, Shreyas Ananthan, Zhijun Zhang, Kyle Hutchings, Peng Wang, Weiguo Chen, and Lin Chen
Wind Energ. Sci., 2, 229–239, https://doi.org/10.5194/wes-2-229-2017,https://doi.org/10.5194/wes-2-229-2017, 2017
Short summary
Wind tunnel tests with combined pitch and free-floating flap control: data-driven iterative feedforward controller tuning
Sachin T. Navalkar, Lars O. Bernhammer, Jurij Sodja, Edwin van Solingen, Gijs A. M. van Kuik, and Jan-Willem van Wingerden
Wind Energ. Sci., 1, 205–220, https://doi.org/10.5194/wes-1-205-2016,https://doi.org/10.5194/wes-1-205-2016, 2016
Short summary
Cited articles
Aho, J., Buckspan, A., Laks, J., Fleming, P. A., Jeong, Y., Dunne, F.,
Churchfield, M., Pao, L. Y., and Johnson, K.: A tutorial of wind turbine
control for supporting grid frequency through active power control, in:
American Control Conference, IEEE, Montreal, QC, Canada, 3120–3131,
https://doi.org/10.1109/ACC.2012.6315180, 2012.
a
Aho, J., Pao, L. Y., and Fleming, P. A.: An active power control system for
wind turbines capable of primary and secondary frequency control for
supporting grid reliability, in: 51st AIAA Aerospace Sciences Meeting
including the New Horizons Forum and Aerospace Exposition, Grapevine, Texas, 1–13,
https://doi.org/10.2514/6.2013-456, 2013.
a
Aho, J., Fleming, P. A., and Pao, L. Y.: Active power control of wind
turbines
for ancillary services: A comparison of pitch and torque control
methodologies, in: American Control Conference, IEEE, Boston, MA, USA, 1407–1412,
https://doi.org/10.1109/ACC.2016.7525114, 2016.
a,
b
Annoni, J., Gebraad, P. M. O., Scholbrock, A. K., Fleming, P. A., and van
Wingerden, J.-W.: Analysis of axial-induction-based wind plant control using
an engineering and a high-order wind plant model, Wind Energy, 19,
1135–1150,
https://doi.org/10.1002/we.1891, 2016.
a
Barthelmie, R. J., Hansen, K., Frandsen, S. T., Rathmann, O., Schepers,
J. G.,
Schlez, W., Phillips, J., Rados, K., Zervos, A., Politis, E. S., and
Chaviaropoulos, P. K.: Modelling and measuring flow and wind turbine wakes in
large wind farms offshore, Wind Energy, 12, 431–444,
https://doi.org/10.1002/we.348,
2009.
a
Bay, C. J., Annoni, J., Taylor, T., Pao, L., and Johnson, K.: Active power
control for wind farms using distributed model predictive control and nearest
neighbor communication, in: American Control Conference, IEEE, Milwaukee, WI, USA, 682–687,
https://doi.org/10.23919/ACC.2018.8431764, 2018.
a,
b
Boersma, S., Vali, M., Kühn, M., and van Wingerden, J.-W.: Quasi Linear
Parameter Varying modeling for wind farm control using the 2D
Navier-Stokes equations, in: American Control Conference, IEEE, Boston, MA, USA, 4409–4414,
https://doi.org/10.1109/ACC.2016.7525616, 2016.
a
Boersma, S., Doekemeijer, B., Vali, M., Meyers, J., and van Wingerden, J.-W.:
A control-oriented dynamic wind farm model: WFSim, Wind Energ. Sci., 3,
75–95,
https://doi.org/10.5194/wes-3-75-2018, 2018.
a
Boersma, S., Doekemeijer, B., Siniscalchi-Minna, S., and van Wingerden,
J.-W.:
A constrained wind farm controller providing secondary frequency regulation:
An LES study, Renew. Energ., 134, 639–652,
https://doi.org/10.1016/j.renene.2018.11.031, 2019.
a
Campagnolo, F., Petrović, V., Schreiber, J., Nanos, E. M., Croce, A., and
Bottasso, C. L.: Wind tunnel testing of a closed-loop wake deflection
controller for wind farm power maximization, J. Phys. Conf.
Ser., 753, 032006,
https://doi.org/10.1088/1742-6596/753/3/032006, 2016.
a
Ciri, U., Rotea, M., Santoni, C., and Leonardi, S.: Large-eddy simulations
with
extremum-seeking control for individual wind turbine power optimization, Wind
Energy, 20, 1617–1634,
https://doi.org/10.1002/we.2112, 2017.
a,
b
de Alegria, I. M., Andreu, J., Martin, J. L., Ibanez, P., Villate, J. L., and
Camblong, H.: Connection requirements for wind farms: A survey on technical
requierements and regulation, Renewable and Sustainable Energy Reviews, 11,
1858–1872,
https://doi.org/10.1016/j.rser.2006.01.008, 2007.
a
Doekemeijer, B. M., Boersma, S., Pao, L. Y., Knudsen, T., and van Wingerden,
J.-W.: Online model calibration for a simplified LES model in pursuit of
real-time closed-loop wind farm control, Wind Energ. Sci., 3, 749–765,
https://doi.org/10.5194/wes-3-749-2018, 2018.
a
Fleming, P. A., Annoni, J., Shah, J. J., Wang, L., Ananthan, S., Zhang, Z.,
Hutchings, K., Wang, P., Chen, W., and Chen, L.: Field test of wake steering
at an offshore wind farm, Wind Energ. Sci., 2, 229–239,
https://doi.org/10.5194/wes-2-229-2017, 2017.
a
Fleming, P. A., Aho, J., Gebraad, P., Pao, L. Y., and Zhang, Y.:
Computational fluid dynamics simulation study of active power control in wind
plants, in: American Control Conference, IEEE, Boston, MA, USA, 1413–1420,
https://doi.org/10.1109/ACC.2016.7525115, 2016.
a,
b,
c,
d,
e,
f,
g
Frandsen, S.: Turbulence and turbulence-generated structural loading in wind
turbine clusters, PhD thesis, Risø-R-1188(EN), Technical University of
Denmark, 2007. a
Gasch, R. and Twele, J.: Wind power plants: fundamentals, design,
construction and operation, 2nd edn., Springer Science & Business Media,
2011.
a,
b
Gebraad, P. M. O. and van Wingerden, J.-W.: Maximum power-point tracking
control for wind farms, Wind Energy, 18, 429–447,
https://doi.org/10.1002/we.1706,
2015.
a
Gebraad, P. M. O., Teeuwisse, F. W., van Wingerden, J.-W., Fleming, P. A.,
Ruben, S. D., Marden, J. R., and Pao, L. Y.: Wind plant power optimization
through yaw control using a parametric model for wake effects: a CFD
simulation study, Wind Energy, 19, 95–114,
https://doi.org/10.1002/we.1822, we.1822,
2016.
a
IEC: International Electrotechnical Commission, Wind turbines – Part 1:
Design requirements, IEC 61400-1:2005(E), Geneva, Switzerland, 3rd edn.,
2005. a
Jensen, T. N., Knudsen, T., and Bak, T.: Fatigue minimising power reference
control of a de-rated wind farm, J. Phys. Conf. Ser., 753,
052022,
https://doi.org/10.1088/1742-6596/753/5/052022, 2016.
a
Jonkman, J. M. and Buhl, M. L.: FAST manual user's guide, National Renewable
Energy Laboratory, Golden, CO, USA, NREL report No. NREL/EL-500-38230, 2005.
a,
b
Kanev, S. K., Savenije, F. J., and Engels, W. P.: Active wake control: An
approach to optimize the lifetime operation of wind farms, Wind Energy, 21,
488–501,
https://doi.org/10.1002/we.2173, 2018.
a
Knudsen, T., Bak, T., and Svenstrup, M.: Survey of wind farm control: power
and
fatigue optimization, Wind Energy, 18, 1333–1351,
https://doi.org/10.1002/we.1760,
2015.
a
Marden, J. R., Ruben, S. D., and Pao, L. Y.: A model-free approach to wind
farm
control using game theoretic methods, IEEE T. Cont. Syst.
T., 21, 1207–1214,
https://doi.org/10.1109/TCST.2013.2257780, 2013.
a
Maronga, B., Gryschka, M., Heinze, R., Hoffmann, F., Kanani-Sühring, F.,
Keck, M., Ketelsen, K., Letzel, M. O., Sühring, M., and Raasch, S.: The
Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric
and oceanic flows: model formulation, recent developments, and future
perspectives, Geosci. Model Dev., 8, 2515–2551,
https://doi.org/10.5194/gmd-8-2515-2015, 2015.
a,
b
Meyers, J. and Meneveau, C.: Large Eddy Simulations of large wind-turbine
arrays in the atmospheric boundary layer, 48th AIAA Aerospace Sciences
Meeting Including the New Horizons Forum and Aerospace Exposition, Aerospace
Sciences Meetings, Orlando, Florida,
https://doi.org/10.2514/6.2010-827, 2010.
a
Munters, W. and Meyers, J.: Dynamic strategies for yaw and induction control
of
wind farms based on large-eddy simulation and optimization, Energies, 11,
177,
https://doi.org/10.3390/en11010177, 2018.
a,
b
Petrović, V., Schottler, J., Neunaber, I., Hölling, M., and Kühn,
M.: Wind tunnel validation of a closed loop active power control for wind
farms, J. Phys. Conf. Ser., 1037, 032020,
https://doi.org/10.1088/1742-6596/1037/3/032020, 2018.
a
Pilong, C.: PJM Manual 12: Balancing Operations, 30th edn., PJM, Audubon, PA,
USA, 2013. a
Porté-Agel, F., Wu, Y.-T., Lu, H., and Conzemius, R. J.: Large-eddy
simulation of atmospheric boundary layer flow through wind turbines and wind
farms, J. Wind Eng. Ind. Aerod., 99, 154–168,
https://doi.org/10.1016/j.jweia.2011.01.011, 2011.
a
Rott, A., Boersma, S., van Wingerden, J.-W., and Kühn, M.: Dynamic flow
model for real-time application in wind farm control, J. Phys.
Conf. Ser., 854, 012039,
https://doi.org/10.1088/1742-6596/854/1/012039, 2017.
a
Sanderse, B.: Aerodynamics of wind turbine wakes, Energy Research Center of
the Netherlands (ECN), ECN-E-09-016, Petten, the Netherlands, Tech. Rep.
2009. a
Sarlak, H.: Large eddy simulation of turbulent flows in wind energy, PhD
thesis, DTU Wind Energy PhD-0037(EN), Technical University of Denmark,
2014. a
Schlipf, D., Schlipf, D. J., and Kühn, M.: Nonlinear model predictive
control of wind turbines using LIDAR, Wind Energy, 16, 1107–1129,
https://doi.org/10.1002/we.1533, 2013.
a,
b,
c,
d,
e
Shapiro, C. R., Bauweraerts, P., Meyers, J., Meneveau, C., and Gayme, D. F.:
Model-based receding horizon control of wind farms for secondary frequency
regulation, Wind Energy, 20, 1261–1275,
https://doi.org/10.1002/we.2093, 2017.
a,
b,
c,
d
Soleimanzadeh, M., Wisniewski, R., and Johnson, K.: A distributed
optimization
framework for wind farms, J. Wind Eng. Ind.
Aerod., 123, 88–98,
https://doi.org/10.1016/j.jweia.2013.08.011, 2013.
a
Spudić, V., Conte, C., Baotić, M., and Morari, M.: Cooperative
distributed model predictive control for wind farms, Optim. Contr.
Appl. Met., 36, 333–352,
https://doi.org/10.1002/oca.2136, 2015.
a
Vali, M., van Wingerden, J.-W., and Kühn, M.: Optimal multivariable
individual pitch control for load reduction of large wind turbines, in:
American Control Conference, IEEE, Boston, MA, USA, 3163–3169,
https://doi.org/10.1109/ACC.2016.7525404, 2016.
a
Vali, M., Vollmer, L., Petrović, V., and Kühn, M.: A closed-loop wind
farm control framework for maximization of wind farm power production, in:
Wind Energy Science Conference, EAWE, Lyngby, Denmark, 2017. a
Vali, M., Petrović, V., Boersma, S., van Wingerden, J.-W., Pao, L. Y.,
and
Kühn, M.: Model predictive active power control of waked wind farms, in:
American Control Conference, IEEE, Milwaukee, WI, USA, 707–714,
https://doi.org/10.23919/ACC.2018.8431391,
2018a.
a,
b,
c,
d
Vali, M., Petrović, V., Steinfeld, G., Pao, L. Y., and Kühn, M.:
Large-eddy simulation study of wind farm active power control with a
coordinated load distribution, J. Phys. Conf. Ser., 1037,
032018,
https://doi.org/10.1088/1742-6596/1037/3/032018, 2018b.
a,
b,
c,
d,
e,
f,
g,
h
Vali, M., Petrović, V., Boersma, S., van Wingerden, J.-W., Pao, L. Y.,
and
Kühn, M.: Adjoint-based model predictive control for optimal energy
extraction in waked wind farms, Control Eng. Pract., 84, 48–62,
https://doi.org/10.1016/j.conengprac.2018.11.005, 2019.
a,
b,
c,
d
van Dijk, M. T., van Wingerden, J.-W., Ashuri, T., and Li, Y.: Wind farm
multi-objective wake redirection for optimizing power production and loads,
Energy, 121, 561–569,
https://doi.org/10.1016/j.energy.2017.01.051, 2017.
a
van Wingerden, J.-W., Pao, L., Aho, J., and Fleming, P.: Active power control
of waked wind farms, IFAC PapersOnLine, 50, 4484–4491,
https://doi.org/10.1016/j.ifacol.2017.08.378, 2017.
a,
b,
c,
d,
e,
f,
g,
h,
i,
j,
k
Wagenaar, J., Machielse, L., and Schepers, J.: Controlling wind in ECN's
scaled wind farm, Proc. Europe's Premier Wind Energy Event, EWEA, Copenhagen,
Denmark, 685–694, 2012. a
Witha, B., Steinfeld, G., Dörenkämper, M., and Heinemann, D.:
Large-eddy simulation of multiple wakes in offshore wind farms, J.
Phys. Conf. Ser., 555, 012108,
https://doi.org/10.1088/1742-6596/555/1/012108, 2014.
a,
b