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Advanced WEC Controls Webinar June 2016

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Slides from the June 6, 2016, webinar on Advanced WEC Dynamics and Controls, hosted by Sandia National Laboratories for the US Department of Energy. SAND2016-5473 PE

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Advanced WEC Controls Webinar June 2016

  1. 1. Photos placed in horizontal position with even amount of white space between photos and header Photos placed in horizontal position with even amount of white space between photos and header Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. Advanced WEC Dynamics and Controls Ryan Coe (rcoe@sandia.gov) Giorgio Bacelli (gbacell@sandia.gov) June 6, 2016
  2. 2. Outline 1. Project overview/overall motivation 2. Control comparison 3. Control basics 4. Testing overview 5. Testing results 6. Model validation 7. Future work 8. Questions/discussion
  3. 3. PROJECT OVERVIEW Advanced WEC Dynamics and Controls 3
  4. 4. Project motivation  Numerous studies have shown large benefits of more advanced control of WECs (e.g., Hals et al. showed 330% absorption increase)  Most studies rely on significant simplifications and assumptions  Availability of incoming wave foreknowledge  1-DOF motion  Linear or perfectly know hydrodynamics  No sensor noise  Unlimited actuator performance 4 Project goal: accelerate/support usage of advanced WEC control by developers
  5. 5. Project objectives  Use numerical modeling and novel laboratory testing methods to quantitatively compare a variety of control strategies: advanced system identification methods for richer results (better numerical models and better controls)  Produce data, analyses and methodologies that assist developers in selecting and designing the best control system for their device: provide developers with the information needed to make informed decisions about their specific strategy on PTO control  Use numerical modeling and testing to determine the degree to which these control strategies are device agnostic: broadly applicable quantitative results, methods and best practices applicable to a wide range of devices  Develop strategies to reduce loads, address fatigue and to handle extreme conditions: reduce loads and high-frequency vibration in both operational and extreme conditions  Full wave-to-wire control: absorption, generation, power-electronics and transmission considered in control design  Develop novel control strategies and design methodologies: leverage Sandia’s control expertise from aerospace, defense and robotics to develop novel WEC control approaches 5
  6. 6. WEC CONTROLS COMPARISON Advanced WEC Dynamics and Controls 6
  7. 7. Control strategy comparison 7 Initial comparison completed and published (METS and SAND report) • Multiple novel strategies applied to WECs • Relative power improvement offered by 8 control strategies • “Cost-to-implement” metrics • Roadmap to WEC control w/ in-depth discussion/instructions for implementation http://energy.sandia.gov/wordpress/../wp-content/uploads/dlm_uploads/2016/06/SAND2016-4293.pdf
  8. 8. Advanced WEC Control  What is the potential of control systems in WECs?
  9. 9. Buoy design 9 Final design Axisymmetric shapes Absorption: comparison
  10. 10. Comparison of Control Strategies  Baseline (Resistive)  Model Predictive Control (MPC)  Dynamic Programming (DP)  Shape Based (SB) Control  Linear Quadratic (LQ) Control  PDC3  Latching 10
  11. 11. Model Predictive Control  Optimization based control strategy  Can be computationally expensive  The control signal is optimal for the predicted excitation force for a linear system.  Requires estimator/predictor  If the prediction is perfect, the control algorithm provides the maximum energy absorption  The control algorithm is capable including constraints (motion, force) in the formulation of the optimization problem  Requires PTO capable of generating reactive power  Requires energy storage 11
  12. 12. Dynamic Programming 12  It can be implemented for nonlinear systems  Optimization based control strategy  Computationally very expensive  The control signal is optimal for the predicted excitation force for a linear system.  Requires estimator/predictor  If the prediction is perfect, the control algorithm provides the maximum energy absorption  The control algorithm is capable including constraints (motion, force) in the formulation of the optimization problem  Requires PTO capable of generating reactive power  Requires energy storage
  13. 13. Shape Based Control  It can be implemented for nonlinear systems  Optimization based control strategy  Computationally very expensive, but more efficient than DP  The control signal is optimal for the predicted excitation force for a linear system.  Requires estimator/predictor  If the prediction is perfect, the control algorithm provides the maximum energy absorption  The control algorithm is capable including constraints (motion, force) in the formulation of the optimization problem  Requires PTO capable of generating reactive power  Requires energy storage 13
  14. 14. Linear Quadratic Control  Pure feedback control strategy  Computationally inexpensive (matrix multiplication)  Optimal feedback gain is obtained by offline optimization  Linear WEC model  LQ feedback control is well known for good properties (stability, robustness to parameters uncertainty,…)  Requires PTO capable of generating reactive power  Requires energy storage  NOT capable of dealing with constraints 14
  15. 15. PDC3  Potential to demonstrate actual realization of CC control design  Implementation will be fundamentally novel and first practical approximation (scheduled for next FY17)  Wave foreknowledge is not required  Method is computationally fast and potentially easy to implement  Uses linear WEC model  Fundamentally feedback control strategy (PD loops)  Requires PTO capable of generating reactive power  Requires energy storage  Expansion of strategy to multi-DOF’s and more nonlinear cases is essential in order to understand how well strategy can work on real world systems 15
  16. 16. Latching  It is a switching control strategy  It does not require model of the WEC for the calculation for the control signal (in its simplest form)  It can be used also for nonlinear systems  It may require prediction of wave elevation/excitation force to improve performance  It does not require PTO capable of generating reactive power  Does not require energy storage  NOT capable of dealing with constraints 16
  17. 17. Sea states 17
  18. 18. Summary of results 18 Resistive CCC Latching LQG PDC3 MPC DP SB Averagepower-net[kW] 0 10 20 30 40 50 60 Resistive CCC Latching LQG PDC3 MPC DP SB Peakforce[N] 0 1000 2000 3000 4000 5000 6000 All units in metric
  19. 19. Summary of results (Sample time-series) 19 Velocity vs Excitation force
  20. 20. Summary of results (Sample time-series) 20 Actuator force vs Excitation force
  21. 21. Summary of results (Sample time-series) 21 Instantaneous absorbed power and excitation force
  22. 22. Nonlinear WEC model Nonlinear configurations  CONFIG-D3:  Quadratic drag  Actuator stroke length = 0.5m  Actuator max force = 8.0 kN  CONFIG-D4:  Quadratic drag  Actuator stroke length = 0.5m  Actuator max force = 2.7 kN Controllers (linearized control model)  Resistive  MPC  LQ  Latching 22
  23. 23. Nonlinear WEC model 23 Note: Plant (WEC) models are nonlinear; control models are linear Linear controllers perform well with (mild) nonlinear devices
  24. 24. CONTROL BASICS Advanced WEC Dynamics and Controls 24
  25. 25. Control systems design 25 WEC EXAMPLE: CC conjugate control Ideal case (simulation) Wave excitation force (𝐹𝑒) Controller Reference velocity 𝑣 𝑟𝑒𝑓 𝑣
  26. 26. Control systems design 26 WEC EXAMPLE: CC conjugate control A more “Realistic” situation Wave excitation force (𝐹𝑒) (Unknown) Noise Sensors Controller State estimator Reference velocity 𝐹𝑒 𝑣 𝑟𝑒𝑓 𝑣 Velocity calc.
  27. 27. Control systems design  Control model:  If control algorithm is based on a model of the system (i.e. model based control), then:  First principle model --> from theory  System identification --> from data 27 𝐹𝑜𝑝𝑡 = 𝑍𝑖 ∗ = 𝑅 𝜔 + 𝐵 + 𝑖 𝜔 𝑀 + 𝑀 𝑎 𝜔 − 𝑘 𝜔 ∗ EXAMPLE: CC conjugate control
  28. 28. Control systems design  Measurements:  Available measurements  May not be possible to measure all the quantities we want (e.g. excitation force, velocity)  State estimator design  Noise in the measurements  Filter  Frequency domain control design 28
  29. 29. Control Structure Interaction (CSI)  What happens when you close the loop?  Even with a simple linear damping, the closed loop system behaves differently  Root locus example: when increasing the damping the poles moves towards RHP zeros -> system unstable 29 𝑦 𝑢 = 𝐺(𝑠) 1 + 𝐺 𝑠 𝐵 𝐺(𝑠) 𝐵 𝑦𝑢
  30. 30. Control Structure Interaction (CSI)  Example: WEC / bridge interaction 30
  31. 31. Control Structure Interaction (CSI)  Closing the loop on velocity using position measurements 31 𝑢 𝜔 = 𝑖𝜔 𝑧 𝜔 When differentiating the position, The noise is amplified by “𝜔” i.e. linearly increasing with frequency
  32. 32. Control Structure Interaction (CSI)  Sensitivity function and “waterbed effect” 32 Feedback Systems: An Introduction for Scientists and Engineers By Karl Johan Aström, Richard M. Murray
  33. 33. Control Structure Interaction (CSI)  Bottom line:  Even the implementation of a “simple” linear damping is not trivial  One simple approach  Low-pass filter  CAREFUL ABOUT THE PHASE! 33 𝐺 𝑠 = 1 1 + 𝑠 Cut-off frequency is 1rad/s Phase is ~5degree at 0.1rad/s
  34. 34. WAVE TANK TESTING Advanced WEC Dynamics and Controls 34
  35. 35. Test objectives “Traditional” decoupled-system testing • Radiation/diffraction • Monochromatic waves Multi-sine, multi-input testing • Excite system w/ both inputs (waves and actuator) • Band-width-limited multi-sine signals “At-sea” testing • Excite system w/ both inputs (waves and actuator) • Idealized wave spectra 35
  36. 36. Test objectives “Traditional” decoupled- system testing Multi-sine, multi-input testing “At-sea” testing 36 Can the approach used in other fields for system identification provide better results for WECs? Can tests that can be conducted at- sea (i.e. cannot control incoming waves) provide sufficient data to model the dynamic system (e.g. to update the model intermittently)? What subtleties specific to WECs need to be considered for designing a better wave tank test?
  37. 37. Test hardware – WEC device 37 • Designed as WEC controls test-bed • Not intended for full-scale • Platform to implement, validate and assess control strategies • Multiple configurations • 1-DOF, 3-DOF, 5-DOF • De-ballast for increased hydro nonlinearity • ~1/17th scale • Displacement: 858 kg
  38. 38. Test hardware – WEC device 38 Weldment Motor stators Vertical carriages Access ladder 6" down-tube PCC tower Motor sliders Planar motion table Ballast plates U-Joint Rotation lockout bars Wave seal Pressure transducers
  39. 39. Test hardware – sensors 39Pressure senors Actuator load cell Surge load cell Heave lock-out load cell 3-axis accelerometer Position-sensing diode Motor position resolver Seismic accelerometer (on bridge) • Acquired at 50 Hz • NI cRIO system coupled to separate wave probe systems via TTL trigger and GPS timestamping
  40. 40. Test hardware – wave basin 40 Maneuvering and Seakeeping (MASK) basin Naval Surface Warfare Center, Carderock Division (NSWCCD) • Built 1962 • Dimensions: 106x76x6m deep • Updated wavemakers in 2013 • 216 individual flaps • Peak wave power is approximately 1MW
  41. 41. Test hardware – basin layout 41 𝑥 𝑦 𝑧 Design factors: • Advanced radiation modeling • Hardware-in-the-loop wave propagation/prediction • Directional seas
  42. 42. Test hardware – wave probes 42  Sonic probes (green)  Tough Senix TSPC-30S1  20 Hz  Capacitive probes (magenta)  OSSI Wave Staffs  50 Hz  Resistive probes (red)  EDL wave-wires  50 Hz
  43. 43. TESTING RESULTS Advanced WEC Dynamics and Controls 43
  44. 44. Testing  Excitation force model  Device locked; 𝐻 𝜔 = 𝐹𝑙𝑜𝑐𝑘 𝜔 /𝜂 𝜔  Radiation force model  No waves; 𝑍 𝑟 𝜔 = 𝐹𝑃𝑇𝑂 𝜔 /𝑢 𝜔 − 𝐵 − 𝑖 𝜔𝑀 − 𝐾 𝜔  System identification  2 inputs / 1 output system  Multisine force on actuator / Multisine wave spectra  Band limited white noise on actuator / Bretschneider wave spectra  Why did we use periodic signals?  Leakage  Better signal to noise ratio – averaging over periods  Smoother spectrum (no dips)  Nonlinearity detection 44
  45. 45. Testing  System identification 45 References: • Davidson, J., Giorgi, S. and Ringwood, J.V.. Identification of wave tank models from numerical wave tank data – Part 1: NWT identification tests. IEEE Trans. on Sustainable Energy, in press., 2016 • Giorgi, S., Davidson, J. and Ringwood, J.V.. Identification of wave tank models from numerical wave tank data – Part 2: Data-based model determination. IEEE Trans. on Sustainable Energy, in press., 2016 Plant (WEC) Multi Input Single Output (MISO) system Output: 𝑧 Input 1: FPTO Input 2: waves
  46. 46. Testing 46 Repeating vs non-repeating spectra Water surface elevation: Bretschneider spectrum, repeat period Trep= 5minutes NOTE: Measurements from a capacitive wave probe Detailed view of the overlapped time series
  47. 47. Testing results 47 Repeating vs non-repeating spectra Bretschneider No Spectrum leakage for period signals Detailed view # harmonic component Power
  48. 48. Testing results 48 Repeating vs non-repeating spectra Period T=5min, total time 15 min Period T=2hrs, total time 30 min Bretschneider “Smoother” spectrum (smaller dips in the spectral density) Better S/N ratio • Measurements from a capacitive wave probe • spectra have not been smoothed, filtered, averaged, etc,.. NOTEs: 𝜔 𝑆(𝜔)
  49. 49. Testing  Excitation FRF: 𝐻 𝜔 = 𝐹𝑙𝑜𝑐𝑘 𝜔 / 𝜂 𝜔 49 𝐻 𝜔 𝜂 𝑡 𝐹𝑙𝑜𝑐𝑘(𝑡) 𝐹𝑙𝑜𝑐𝑘 𝜔 𝜂 𝜔 DFT (FFT) 𝜂 𝑡 , 𝐹𝑙𝑜𝑐𝑘(𝑡) trimmed at integer multiple of period
  50. 50. Testing  Excitation FRF: 𝐻 𝜔 = 𝐹𝑙𝑜𝑐𝑘 𝜔 / 𝜂 𝜔 50
  51. 51. Testing  Radiation FRF: 51 𝑍 𝑟 𝜔 = 𝑅 𝜔 + 𝑖𝜔 𝑀 𝑎 𝜔 = 𝐹𝑃𝑇𝑂 𝜔 𝑢 𝜔 − 𝐵 − 𝑖 𝜔𝑀 − 𝐾 𝜔 𝑅 = Radiation damping 𝑀 𝑎 = Added mass 𝑢 = velocity 𝐹𝑃𝑇𝑂 = PTO force 𝐵 = linear friction/dissipation 𝑀= mass 𝐾 = hydrostatic restoring coeff 𝑍 𝑟 𝜔 𝑢 𝑡 𝐹𝑃𝑇𝑂(𝑡) 𝐹𝑃𝑇𝑂 𝜔 𝑢 𝜔 DFT (FFT) 𝑢 𝑡 , 𝐹𝑃𝑇𝑂(𝑡) trimmed at integer multiple of period
  52. 52. Testing  Radiation FRF: 𝑍 𝑟 𝜔 = 𝑅 𝜔 + 𝑖𝜔 𝑀 𝑎 𝜔 52
  53. 53. Testing  Models comparison from different experiments 53 𝐹𝑃𝑇𝑂 𝜔 𝑢 𝜔 = 𝑍𝑖 = 𝑅 𝜔 + 𝐵 + 𝑖 𝜔 𝑀 + 𝑀 𝑎 𝜔 − 𝐾 𝜔 Intrinsic impedance Experiments at different values of input power and spectra give same model Sample Input force spectra
  54. 54. Testing  Pressure based frequency response functions  Why use pressure?  Pressure relatively easy to measure  Pressure sensors located on the device  Excitation pressure to force TF is causal 54
  55. 55. Testing  At sea system identification  It is not possible to use periodic waves at sea  Still have control on the PTO force, depending on PTO capabilities  Band limited white noise BLWN  Multisine  Modulate PTO damping  … 55 Plant (WEC) Output: 𝑧, 𝑧, 𝑃 Input 1: FPTO (Multisine, BLWN) Input 2: (real) waves BLWN = band limited white noise
  56. 56. Testing  Waterline in conical section  nonlinear modeling: In progress 56 𝑧 Asymmetric response: nonlinear
  57. 57. FUTURE WORK Advanced WEC Dynamics and Controls 57
  58. 58. Testing results  State estimator  Using wave tank experiments  At sea 58 WEC 𝐺𝑟𝑃𝑒 𝐹𝑒 𝑧 𝐺𝑒 𝐹𝑃𝑇𝑂 + + + + 𝑃 Model set-up: 𝑃𝑒 = excitation pressure 𝑃 = measured pressure 𝐺𝑒 = exc pressure to Force TF 𝐺𝑟 = rad velocity to pressure TF 𝑃𝑒 = Unknown input
  59. 59. Out-years  Real-time control  Expand study to  Nonlinear modeling/control  Additional degrees-of-freedom  Include power-electronics and structural modeling  Dynamics and control of 2nd device (considering WEC-Sim validation device, “FOSWEC) 59
  60. 60. Publications/disseminations  Control strategy development [3,4,5,6]  Control-structure interaction [2]  Control strategy comparison [3,4]  Wave tank test report w/ testing data (in-prep)  Model development [7,8]  Updated control comparison, based on model from tank testing (in-prep)  Pressure-excitation state-estimator (in-prep)  Proposed: 2017 METS WEC dynamics and controls workshop 60
  61. 61. Thank you This research was made possible by support from the Department of Energy’s Energy Efficiency and Renewable Energy Office’s Wind and Water Power Program. Sandia National Laboratories is a multiprogram laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. 61 Project team: Alison LaBonte (DOE) Jeff Rieks (DOE) Giorgio Bacelli (SNL) Ryan Coe (SNL) Dave Wilson (SNL) David Patterson (SNL) Miguel Quintero (NSWCCD) Dave Newborn (NSWCCD) Calvin Krishen (NSWCCD) Mark Monda (SNL) Kevin Dullea (SNL) Dennis Wilder (SNL) Steven Spencer (SNL) Tim Blada (SNL) Pat Barney (SNL) Mike Kuehl (SNL) Mike Salazar (SNL) Ossama Abdelkhalik (MTU) Rush Robinett (MTU) Umesh Korde (SNL) Diana Bull (SNL) Tim Crawford (SNL)
  62. 62. References [1] ——, “Estimation of excitation force on wave energy converters using pressure measurements,” in OCEANS2016, submitted. Monterey, CA: IEEE, September 2016. [2] D. Wilson, G. Bacelli, R. G. Coe, R. D. R. III, G. Thomas, D. Linehan, D. Newborn, and M. Quintero, “WEC and support bridge control structural dynamic interaction analysis,” in METS2016, Washington, D.C., April 2016. [3] D. Wilson, G. Bacelli, R. G. Coe, D. L. Bull, O. Abdelkhalik, U. A. Korde, and R. D. R. III, “A comparison of WEC control strategies,” Sandia National Labs, Albuquerque, New Mexico, Tech. Rep. SAND2016-4293, April 2016 2016. [4] G. Bacelli, R. G. Coe, D. Wilson, O. A. U. A. Korde, R. D. R. III, and D. L. Bull, “A comparison of WEC control strategies for a linear WEC model,” in METS2016, Washington, D.C., April 2016. [5] O. Abdelkhalik, R. Robinett, S. Zou, G. Bacelli, R. Coe, D. Bull, D. Wilson, and U. Korde, “On the control design of wave energy converters with wave prediction,” Journal of Ocean Engineering and Marine Energy, pp. 1–11, 2016. [6] ——, “A dynamic programming approach for control optimization of wave energy converters,” in Ocean Engineering, in review, 2016. [7] D. L. Bull, R. G. Coe, M. Monda, K. Dullea, G. Bacelli, and D. Patterson, “Design of a physical point-absorbing WEC model on which multiple control strategies will be tested at large scale in the MASK basin,” in International Offshore and Polar Engineering Conference (ISOPE2015), Kona, HI, 2015. [8] R. G. Coe and D. L. Bull, “Sensitivity of a wave energy converter dynamics model to nonlinear hydrostatic models,” in Proceedings of the ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering (OMAE2015). St. John’s, Newfoundland: ASME, 2015. [9] D. Patterson, D. Bull, G. Bacelli, and R. Coe, “Instrumentation of a WEC device for controls testing,” in Proceedings of the 3rd Marine Energy Technology Symposium (METS2015), Washington DC, Apr. 2015. [10] R. G. Coe and D. L. Bull, “Nonlinear time-domain performance model for a wave energy converter in three dimensions,” in OCEANS2014. St. John’s, Canada: IEEE, 2014. 62

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