Control strategies applied to Wave Energy Converters
Paolino Tona
IFP Energies nouvelles
09/09/2019
Stockholm
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
IFP Energies nouvelles at a glance
09/09/2019
One of the only French public research bodies to self-fund
over 50% of its budget
â¬128.3M budget allocation (2017)
â¬152M own resources (2017)
50% of budget devoted to NETs
1,622 1,119 R&I engineers and technicians employees
2 facilities:
Rueil and Solaize
IFP Energies nouvelles (IFPEN) is a French public-sector research and training center active in the fields of energy, transport and the environment
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
IFPEN strategic priorities and core skills
09/09/2019
SUSTAINABLE MOBILITY
Developing effective, environmentally-
friendly solutions for the transport sector
RESPONSIBLE
OIL AND GAS
Proposing technologies that meet
the demand for energy and chemical products
while improving energy efficiency and
reducing the environmental impact
NEW ENERGIES
Producing fuels, chemical intermediates
and energy from renewable
sources
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
Offshore wind and ocean energies
09/09/2019
Estimation, prediction and control for WECs
Simulation and design tools for fixed and floating foundations
⢠A project dedicated to WEC control was started in 2013, focusing on ⢠point-absorbers
⢠model predictive control (MPC) solutions
building on IFPEN expertise in ⢠design and simulation of floating structures
⢠design and implementation of control strategies (for the automotive and process industries, namely)
Estimation and control for wind turbines
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
Control of Wave Energy Converters @IFPEN
⢠Motivation ⢠Control as a key factor to optimize energy production of WECs
⢠Guiding principles ⢠Reduce the gap between theory and practice, maintaining a good balance
between academic rigor and industrial relevance ⢠15 publications (4 in journals) + 8 patent applications (7 granted so far)
⢠Propose an approach as generic as possible, with modular solutions allowing to ⢠Develop control systems of different complexity (i.e. adaptive PI control vs. MPC)
⢠Adapt them to different machines
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⢠Milestones and key events ⢠Development of a nonlinear MPC algorithm taking into account
PTO efficiency for reactive-control capable point absorbers (2014)
⢠Validation of a complete nonlinear MPC system able to run in real-time (2015)
⢠Start-up of the H2020 UPWAVE project with Wavestar (2016)
⢠Start-up of the ADEME S3 project with SBM Offshore (2017)
⢠Participation to the WEC Control Competition (1st place for the simulation phase in 2019, experimental phase under evaluation)
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
Some reminders on hydrodynamic control
⢠In many WECs, the PTO is actively controlled in order to apply forces that change the dynamic response of the primary converter to the forces exerted by the waves
⢠Such active control may be used to improve the harvesting and/or to reduce the forces exerted on the components of the WEC ⢠According to conventional hydrodynamic control theory, maximum recovery
takes place when captor speed oscillates in phase with wave excitation force
⢠The most significant gains (at least for narrow-banded WECs) are obtained when it is possible to operate the PTO not only as a generator but also as a motor, making it possible to implement "reactive" control
09/09/2019
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
ðð(ð¡) =1
ð ððððð ð¡ ð£(ð¡)dð¡
ð
0
Some reminders on hydrodynamic control
⢠In principle, reactive control, even in its simplest forms, allows to better adapt WEC natural response band to the current sea state
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ð£ ð¡
1. 2. 1. P or damping control
ðððð(ð¡) = ðµðððð£(ð¡)
2. PI or damping-spring control
ðððð ð¡ = ðµðððð£ ð¡ + ðŸððð ð£ ð ððð¡
0
⢠However, control tuning in a real-life setting requires some care ⢠For instance, gains tuned to maximize mean mechanical power
may even lead to negative net electricity production, as the effect of PTO efficiency ð is neglected
ðð(ð¡) =1
ð ðððð ð¡ ð£(ð¡)dð¡
ð
0
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
On the potential of advanced control: WECCCOMP case study
09/09/2019
⢠Energy-maximizing control for a small-scale Wavestar device, as in WECCCOMP
⢠ðð,ð: mean electric power for each sea-state k as a function of PTO efficiency ð (normalized w.r.t. the ideal case ð=1)
⢠ðð,ð
â, ðð,ð
+: upper and lower bounds on
optimal solution computed offline using a spectral approach
⢠ðð,ðµðð¡ð: mean electric power obtained with
damping control
⢠ðð,ððð ð: mean electric power obtained assuming ð=1 when ðâ 1
Normalized mean electric power ðð,ð
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
On the potential of advanced control: RM3 case study
⢠Energy-maximizing control for the RM3 device modeled in WEC-Sim
⢠ðð,ð: nomalized mean electric power for each sea-state k as a function of PTO
efficiency ð ðð,ð
â, ðð,ð
+: upper and lower
bounds on the optimal solution
⢠ðð,ðµðð¡ð: mean electric power obtained with
damping control
⢠ðð,ððð ð: mean electric power obtained assuming ð=1 when ðâ 1
⢠Only position constraints considered
⢠See [10] for more details
Normalized mean electric power ðð,ð
⢠Interesting potential for the types of WEC studied in SeaTitan (provided that PTO behavior is correctly taken into account)
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
Is advanced control for WECs mature?
⢠So far, implementation of hydrodynamic control as reported by WEC manufacturers seems to be limited to very simple strategies
09/09/2019
WEC Type DoF Response band Reactive Control Law Sea Trials
Oyster Flap 1 Large No Coulomb Yes
WaveRoller Flap 1 Large No P Yes
Pelamis Attenuator 2 Narrow Yes MIMO (PI ?) Yes
Wavestar Point absorber 1 Narrow Yes PI Yes
ISWEC Rotating mass 2 Narrow Yes PI Yes
CETO Submerged point absorber 1 Narrow No P Yes
CorPower C3 Point absorber 1 Large No P (I by design) In progress
WaveSwing Submerged point absorber 1 Large Yes Velocity tracking In progress
⢠Indeed, of the numerous advanced control strategies presented in the literature: ⢠None has undergone sea trials
⢠Few have been tested in realistic conditions
[As to 2018]
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
Factors slowing the deployment of advanced WEC control
⢠Lack of standardization
⢠Low priority given to control development in the rush to sea installation
⢠PTO limitations in early prototypes
⢠Difficulties in performing small-scale tests including PTOs
⢠Difficulties in developing accurate wave-to-wire simulators for rapid control prototyping
⢠Limitations of classic hydrodynamic control framework, which assumes ⢠Linearity of WEC dynamics
⢠Ideal PTOs, in terms of (infinitely fast) dynamics and (100%) efficiency
⢠Absence of constraints, both on PTO forces and captor motion
⢠Accessibility of non-measurable quantities (such as wave excitation force)
⢠Underestimation of the impact of computational constraints when implementing advanced control strategies in real-time
09/09/2019
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
Model Predictive Control (MPC)
⢠MPC principle 1. Predict system state over a short
future horizon
2. Compute the optimal control sequence maximizing (or minimizing) an objective function over the horizon
3. Apply only the first step of computed control sequence during one period
4. Start over at the next sample time (receding horizon)
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⢠Like a chess player ⢠MPC « looks ahead » to
find the winning strategy
⢠applies one move at time
⢠changes strategy depending on the reaction
⢠MPC is an appealing strategy for WECs as it can be used to maximize an energy-related criterion which takes into consideration the future (predicted) behavior of the system, while complying with its physical constraints (PTO forces, motion)
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
MPC for WECs
⢠MPC is actually a generic name for a family of control strategies, differing in ⢠Nominal WEC model (linear / nonlinear)
⢠Discretization method (of the energy-related criterion) and objective function
⢠Optimization algorithm
which also includes recent MPC-like algorithms based on spectral and pseudo-spectral approaches
⢠Even when the WEC model is linear, translating realistic WEC control objectives into an MPC framework easily results in non-convex optimization problems, which are computationally complex and difficult to solve in real-time
⢠Moreover, most MPC algorithms rely on the computation of predictions of the future action of waves on the device, which is not straightforward in a realistic setting
⢠In this context, experimental tests on a device (or at least hardware-in-the-loop tests with hard real-time constraints) become essential to assess the performance of any MPC and MPC-like strategy
09/09/2019
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
MPC @ IFPEN
A. Online estimation of the (non measurable) wave excitation force ⢠Only accessible WEC measurements (position, velocity, PTO force) are used
⢠Same calibration for large ranges of sea-states
⢠Non-linear terms (such as viscous forces) can be taken into account
B. Short-term prediction of wave excitation force ⢠Only present and past wave excitation moment estimates are used
⢠Automatic âadaptationâ to the current sea-state (same calibration for multiple sea-states)
C. Energy-maximizing MPC, taking into account (non linear) PTO efficiency
⢠Discretization of ðð(ð¡) =1
ð ðððððððð ð¡ ð£(ð¡)dð¡ð
0 with ðððð =
ð if ðððð ð¡ ð£(ð¡) ⥠0
1 ð if ðððð ð¡ ð£ ð¡ < 0
⢠Offline optimization procedure used to choose the parameters of an equivalent convex control problem to be solved on line
A B C
⢠Typical sampling periods (at full scale) A. 10-50 ms
B. 100-500 ms
C. 100-500 ms
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
Some lessons learned during MPC development and validation
⢠IFPEN MPC control system has undergone two experimental test campaigns, on a small scale model of a Wavestar-like device, with different setups
⢠Tests at this scale can be challenging for MPC as ⢠Friction is more significant than at full scale
⢠Time scale is smaller, so are algorithm sampling periods
Wavestar-like device in wave tank, Aalborg University, June 2019
⢠A preliminary analysis of the results of June 2019 tests for WECCCOMP, shows that, despite a badly tuned PTO force control ⢠Wave excitation force can be accurately estimated, except for extreme positions where
peaks and troughs are underestimated or overestimated (but still well captured)
⢠For a given sea state, the same levels of mean electric power obtained in simulation using the nominal design model (up to 20% more than PI control ), can be observed in the experimental tests, and this, across different realizations of the sea state ⢠However, the offline optimal control solution shows that there should be ~15% more to gain
⢠MPC is able to run in real-time in a rapid control prototyping framework where any computation longer than 1ms leads to a CPU overload (and thus to a crash)
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
Sample test results (WECCCOMP SS6)
09/09/2019
10 15 20 25 30 35 40
Time [s]
-15
-10
-5
0
5
10
15
[ N m
]
Estimated (blue) vs measured (red) wave excitation moment
10 15 20 25 30 35 40
Time [s]
-15
-10
-5
0
5
10
15
[ N m
]
PTO torque demand (red) vs applied PTO torque (blue)
10 15 20 25 30 35 40
Time [s]
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
[ r a
d ]
Float position
10 15 20 25 30 35 40
Time [s]
-1
-0.5
0
0.5
1
[ r a
d / s
]
Float velocity (KF estimation)
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
Conclusions and perspectives
⢠Among the advanced control strategies proposed so far, MPC has probably the best potential to improve the electricity production from WECs, in particular the narrow-banded ones
⢠With IFPEN approach, this potential has been proven experimentally at lab scale on a mildly nonlinear fixed-reference point absorber, in the presence of relatively strong constraints on computational time
⢠Simulation findings show that the approach could be profitably extended to other 1-DoF WECs, but in these different contexts, feasibility, robustness and attainable performance must be studied in more depth and confirmed by experimental testing
Hopefully in the framework of collaborations with WEC manufacturers which are crucial to make progress in this field
For its capacity to realize high and fast force demands, accurately and continuously, in generator and motor mode, direct-drive PTO is the best ally for MPC, in what can be a winning technology combination
09/09/2019
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
Acknowledgements
Many thanks to Aleix Arenas and the workshop organizers for this invitation andâŠ
All the best with the SeaTitan project!
09/09/2019
www.ifpenergiesnouvelles.com
@IFPENinnovation
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For further information, please write to: [email protected]
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
WEC control references
a. Korde, U. A. and Ringwood, J. V., Hydrodynamic Control of Wave Energy Devices, Cambridge University Press, 2016
b. Falnes, J., A review of wave-energy extraction, Marine Structures, 20(4):185{201}, 2007
c. Ringwood, J., Control Optimisation and Parametric Design, in Numerical Modelling of Wave Energy Converters, M. Folley, Ed., Academic Press, pp. 229 â 251 , 2016
d. Faedo, N. Olaya, S. and Ringwood, J.V. Optimal control, MPC and MPC-like algorithms for wave energy systems: An overview, IFAC Journal of Systems and Control, Vol.1, pp: 37-56, 2017
e. Wave Energy Scotland (WES), Control Requirements for Wave Energy Converters Landscaping Study, Technical report ref. WES_LS04_ER_Controls, WES, July 2016.
f. Ringwood, J. V., Ferri, F., Ruehl, K., Yu, Y.-H., Coe, R., Bacelli, G., Weber, J. and Kramer, M. M., A competition for WEC control systems, in 12th European Wave and Tidal Energy Conference, 2017
09/09/2019
This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No. 764014
IFPEN references
1. Nguyen, H.-N. and Tona, Wave excitation force estimation for wave energy converters of the point absorber type, IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2017.2747508 , 2017
2. Nguyen, H.-N and Tona, P., Continuously Adaptive PI-Control of Wave Energy Converters under Irregular Sea-State Conditions, In Proceedings of the European Wave and Tidal Energy Conference (EWTEC) 2017
3. Nguyen, H.-N. and Tona, P., An efficiency-aware continuous adaptive proportional-integral velocity-feedback control for wave energy converters, Renewable Energies, Volume 146, February 2020
4. Nguyen, H.-N and Tona, P., Robust Adaptive PI Control of Wave Energy Converters with Uncertain PTO Systems, 57th IEEE Conference on Decision and Control (submitted), Miami, USA, 2018
5. Nguyen, H.-N., Tona, P., and Sabiron, G., Dominant wave frequency and amplitude estimation for adaptive control of wave energy converters, in MTS/IEEE OCEANS 2017 Conference, Aberdeen, U.K., 1978
6. Tona, P., Nguyen, H.-N., Sabiron, G., and Creff, Y., 2015. An efficiency-aware model predictive control strategy for a heaving buoy wave energy converter. Proc. EWTEC2015, Nantes, FR
7. Nguyen, H.-N., Sabiron, G., Tona, P., M. Kramer, Vidal Sánchez, E., 2016. Experimental validation of a nonlinear Model Predictive Control strategy for a wave energy converter prototype. Proc. OMAE 2016, Busan, KR, 2016
8. Nguyen, H.-N. and Tona, P., Short-term wave force prediction for wave energy converter control, Control Engineering Practice, vol. 75, pp. 26â37, 2018
9. Tona, P., Sabiron, G., Nguyen, H.-N., An Energy-maximising MPC Solution to the WEC Control Competition. Proc. OMAE 2019, Glasgow, UK
10. Mérigaud, A. and Tona, P., Spectral control of wave energy converters with non-ideal power take-off systems, IEEE Transactions on Sustainable Energy (submitted), 2020
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