Control strategies in OWC plants

Transcription

1 Control strategies in OWC plants 27/03/2017 TECNALIA By François-Xavier Faÿ

2 Contents Generalities in OWC systems Numerical modelling of the overall system Adaptive control for OWC Rotational speed control Towards control implementation Predictive algorithms for OWC Challenges of predictive algorithms Pressure threshold latching Variable speed control Conclusion

3 Generalities in OWC systems WEC prototypes and evolution Trendy technology Oceanlinx Ocean Energy Mutriku plant LIMPET REWEC Oceantec Pico plant

4 The whole energy conversion chain Generalities in OWC systems Where and how to apply control in the OWC? Interaction wave/device Maximise absorbed power Survivability issues Latching control Turbo-generator group Optimise power production Components reliability Existing solutions Turbine speed control Grid integration Power quality Grid codes compliance Reactive power control Global goal Maximise the energy production from waves, assuring safe operation of the device to decrease the cost of energy. 4

5 Numerical model of the overall system System motion dynamics Cummins equation for an oscillating body oscillating in heave mode mx t = F exc t F rad t F h t F PTO t And in a floating buoy with 2 DoF: m 1 + A,11 x 1 t + A,12 x 2 = F exc,1 t R 11 R 12 ρ w gs 1 x 1 t + Δp S 2 A,21 x 1 + m 2 + A,22 x 2 t = F exc,2 t R 22 R 21 ρ w gs 2 x 2 t Δp S2 In state space form X = x 1 x 2 x 1 x 2 p Ω Rad T ; U = F exc,1 F exc,2 T g T X = AX + BU + f(x, u) Y = CX + DU 2 p c at m = p at S 2 h 0 + Δx p + 1 γ 1 γ Δx γ h 0 + Δx p + 1 Ω = T t T g I Account for non-linearities

6 Numerical model of the overall system Power take-off system Air turbines Dimensionless equations for turbine characterisation o Dimensionless pressure head: Ψ = p atp ρ at Ω 2 d 2 o Dimensionless mass flow rate: Φ = m t ρ at Ω d 3 o Dimensionless power: Π = P t ρ at Ω 3 d 5 o Efficiency: η = P t p at p (m t /ρ) W.K. Tease, J. Lees &A. Hall. Advances in Oscillating Water Column Air Turbine Development. Proceedings of the 7th European Wave and Tidal Energy Conference, Porto, Portugal, 2007 M. Penalba and J. Ringwood, A Review of Wave-to-Wire Models for Wave Energy Converters. Energies 2016

7 Numerical model of the overall system Power take-off system Generators Generator loss model The loss model represents all the losses through the generator from the mechanical to electrical power: Flux weakening region Decrease of maximal extractible torque capacity in order to ensure current and to assure fixed voltage and frequency

8 Adaptive control strategies Optimal buoy operation equipped with Wells turbine Include a damping valve in series with the turbine No stall constraint Find the optimal configuration of turbine speed and valve aperture

9 Adaptive control strategies Rotational speed control Designing a controller Optimal fixed speed controlled by a PI controller Variable speed controlled by a PI controller If the PI gains are tuned with the objective of allowing the speed to vary around the reference optimal speed T t T g = IΩ

10 Generator torque - Nm Pturbine avg - kw Valve aperture - m Adaptive control strategies Rotational speed control Designing a controller Variable speed through turbine efficiency η max = Optimal turbine torque: Π opt Ψ opt Φ opt ; Π = T t ρ at Ω 2 d 5 T t = η max Ψ opt Φ opt ρ at d 5 Ω 2 Variable speed through a torque law T g = a Ω b Where a and b are obtained by fitting the optimal speed giving the highest average Turbine power for a given sea state. So there is a quadratic (optimal) torque law T t = k Ω 2 = T g if we assume P t = P g Power law and Valve diameter 120 data fitted curve Torque laws TL1 TL Rotational speed - rad/s 20 For a Wells turbine For the biradial turbine Rotational speed - rad/s

11 Adaptive control strategies Rotational speed control Some results

12 Adaptive control strategies Kinetic energy storage enabled in the variable speed control

13 Towards control implementation Control environment Online model based control needs excitation force estimation! Design and implementation of a Luenberger observer e k = x k x k e k + 1 = A LC e k The Luenberger gain : L = XC T R L 1 Is computed resolving the Riccati equation AX + XA T XCC T + Q = 0 Framework of a model-based controller Validation of the estimation of the excitation force

14 Towards control implementation Control environment Implementation in the PLC program Linking control developed in Simulink with the PLC program

15 Towards control implementation Control environment PTO scaled test bench validation of variable speed control strategy Resolve scaling uses using Froude criterion on the main quantities λ = D p Dm Torque law to be implemented in the PLC P nom kw Ω nom rpm Motor Generator System total inertia 1,2 kg m 2 Framework for the WEC emulation on the test rig The setup Validation results

16 Towards control implementation Hs Tp Sea state 9/3/2017 1,4 m 12 s Comparison of operational data for the fixed and variable speed controls in Mutriku plant Test campaign 9/3/2017 A numerical model is developed and the optimal reference speed is obtained for fixed speed control A quadratic torque-speed curve is designed for two design points. Fixed reference speed Design point for maximum efficiency Personalised design point

17 Towards control implementation Hs Tp Sea state 9/3/2017 1,4 m 12 s Comparison of operational data for the fixed and variable speed controls in Mutriku plant Test campaign 9/3/2017 Fixed speed Variable speed Water elevation Average Turbine Pressure speed Generator power Water elevation Peak values Turbine Pressure speed Generator power Observations Fixed speed control - produces more - FS sometimes needs energy from the grid - FS shows worst power quality Variable speed control - VS produces 10% less - Continuous production - Smooth power production

18 Predictive control Controlling a WEC Optimum control under resonant condition For a point absorber it is imperative that means are provided for optimum control of the oscillatory motion in order to achieve a maximum of power conversion, P. Falnes How to? Phase and amplitude control Reactive control Latching Model predictive control Issues of optimum control Simplification of linear model Practical implementation Forecasting of quantities A.H. Clément, A. Babarit,. Discrete control of resonant wave energy devices Not recommended in practice B. Teillant, J.C. Gilloteaux, and J. Ringwood, Optimal damping profile for a heaving buoy wave energy converter, 2010

19 Predictive control Challenges of MPC in OWC applications Model Predictive Control Optimise for each time step the control action Best for following a reference High computational cost when accounting for non-linearities The case of the OWC No direct relation between WEC motion and PTO but presence of the air chamber acting as a buffer Highly non-linear: Turbine efficiency, air compressibility Can change the motion indirectly by actuating on the pressure in the chamber via a fast actuating shut-off valve placed in front of the turbine. Equivalent to a latching-like control Effect of the valve, control action u v = {0 1} for the close/open positions : p = u v 2 c at m p at S 2 h 0 + Δx p + 1 P t = u v (η p at ρ p m t )) γ 1 Δx γ γ h 0 + Δx p + 1 Optimise u v at each sampling time in an MPC-like algorithm?

20 Predictive control Pressure threshold latching The solution 4 pressure thresholds to be optimised on-line during the prediction horizon (1 or 2 waves ahead) The control law closes/opens the valve following the optimised pressure thresholds during a replanning period ½ or full wave. Each re-planning, the control vector U = Th 1 Th 2 Th 3 Th 4 T is optimised such that it maximises the cost function max J = P t The control law is then For a compression (Δp*>0) For an expansion (Δp*<0) when p > Th 1 then close valve when p > Th 2 then open valve when p < Th 3 then close valve when p < Th 4 then open valve

21 Turbine Power - kw Dimensionless pressure Excitation Force - 5e-5 [N] & Buoy velocity - [m/s] Predictive control Pressure threshold latching of the floating buoy Results in regular waves The turbine is operated with an optimal fixed speed control. Times Simulation Transitory Sampling Replanning Prediction horizon 1200 s 300 s 0.15 s 1 * Tp 2 * Tp Effect of latching on the chamber pressure Pressure - Ctrl OFF Pressure - Ctrl ON Valve position Th1 Th2 Th3 Th F exc1 - F exc2 DeltaV Ctrl OFF DeltaV Ctrl ON Effect of latching on the buoy's displacement Time - [s] P t - Ctrl OFF P t - Ctrl ON Valve position Effect of latching on the turbine power Time - [s] Hs=2m Tp (s) Pt_av (kw) No opt. Pt_av (kw) Opt. Increase (%) % % % % % % % % % % % % % % % % % Time - [s]

22 Predictive control Pressure threshold latching of the floating buoy Results in irregular waves Optimal fixed speed control for the turbine operation No generator to know the effect of latching only SS Hs [m] Tp [s] Occ [%] Total occurrence selected sea states of BiMEP SS Pt_av (kw) Pt_av (kw) No opt. Opt. Increase (%) % % % % % % % % % % % % % % % % % % % %

23 Predictive control Pressure threshold latching of the floating buoy Observations and critics The annual energy production is increased! BUT Need for wave forecasting or excitation force prediction Reliable numerical model Effective controller without any delay Add reliability issues Need complementary control SS Turb eff (%) No opt. Turb eff (%) Opt. 1 56% 55% 2 54% 53% 3 54% 52% 4 54% 53% 5 55% 54% 6 53% 52% 7 54% 52% 8 54% 52% 9 53% 51% 10 53% 52% 11 54% 52% 12 53% 51% 13 54% 51% 14 53% 51% 15 53% 51% 16 53% 50% 17 54% 51% 18 53% 51% 19 53% 51% 20 53% 50%

24 Evolution of the exponent parameter b Evolution of the slope parameter a Predictive control Variable speed control Optimising the torque law: With the cost function max J = T g = a Ω b P t Under components restrictions on maximum turbine speed and generator torque Iterations Effect of control on the parameters [a,b] Iterations Increase between 3 to 10 % in the generated power Turbine eff (%) No opt. Turbine eff (%) Opt. 44% 53% 46% 52% 48% 53% 45% 53% 44% 54% 47% 53% 49% 54% 48% 54% 48% 53% 47% 53% 50% 54% 51% 54% 50% 54% 49% 54% 48% 54% 51% 54% 50% 55% 50% 55% 49% 54% 51% 55%

25 Pg Increase Generated power - kw Predictive control The curious case of Mutriku Latching - Reg 1.5m RAO - Prony 20th approx RAO Prony s - T 60s Tp - s Base case Optimised 50.00% 40.00% Prediction horizon variation 30.00% 20.00% 10.00% Tph=1Trp Tph=2Trp 0.00% Tp - s Increase up to 2% in the operational range of Tp same observation in irregular waves

26 Predictive control The curious case of Mutriku A new PTO for Mutriku Opera-h2020.eu Biradial turbine diameter: 0,5 m Induction generator: 30 kw Equipped with high-speed valve Safety turbine cut-off speed: 300 rad/s Design point for the base case BASE CASE Predictive speed control Sea states Pg_av - kw Pg_pk - kw Omega_av - rad/s Pg_av - kw Pg_pk - kw Omega_a v - rad/s Increase in Pg % % % % % % % % % % % % % % % Total increase in AEP 6% Valve close time - s No opt. Opt % increase in AEP but maximum power reached in numerous sea states

27 Predictive control The curious case of Mutriku Constrained predictive speed control Opera-h2020.eu The cost function is penalised when the generator nominal power is reached. SS Pt_av - kw Pg_av - kw Pg_pk - kw Omega_a v - rad/s Omega_ pk - rad/s Valve close time - s AEP MWh Decrease on AEP -12%

28 Predictive control Implementation of predictive control in the test bench

29 Conclusions and future steps A full Wave2Wire numerical model has been developed for a buoy and a fixed OWC Rotational speed control Several strategies have been assessed and compared Both have been tested experimentally in a scaled test bench and in real operation at Mutriku Predictive control strategies A pressure latching predictive control strategy has been developped and the benefits and drawbacks have been highlighted A predictive speed control strategy has been developped and tested experimentally Future works will provide operational results of the predictive algorithms in Mutriku

30 François-Xavier FAŸ Marine Renewable Energy Area Energy and Environment Division Tecnalia Research & Innovation Copyright Tecnalia 2017