Robust control of wave energy converters

Energy-maximising controllers for wave energy devices are normally based on linear hydrodynamic device models. Such models ignore nonlinear effects which typically manifest themselves for large device motion (typical in this application) and may also include other modelling errors. In this paper, we present a methodology for reducing the sensitivity to modelling errors and nonlinear effects by the use of a hierarchical robust controller, which also allows good energy maximisation to be recovered through a passivity-based control approach

Robust control of wave energy converters

Energy-maximising controllers for wave energy devices are normally based on linear hydrodynamic device models. Such models ignore nonlinear effects which typically manifest themselves for large device motion (typical in this application) and may also include other modelling errors. In this paper, we present a methodology for reducing the sensitivity to modelling errors and nonlinear effects by the use of a hierarchical robust controller, which also allows good energy maximisation to be recovered through a passivity-based control approach

A power take-off and control strategy in a test wave energy converter for a moderate wave climate

The energy in the waves of oceans and seas can be converted to electricity by different types of Wave Energy Converters (WECs). Wave energy conversion is currently widely studied to contribute to the world’s rising energy needs. This paper describes a point absorber test WEC that was built for electrical energy production in moderate wave climates as can be found in the Belgian part of the North Sea. A robust design was put forward to assess the feasibility of a full electric rotational Power Take-Off (PTO) system. A stable reactive control algorithm was implemented to optimise the absorbed energy from the waves by tuning the natural frequency of the WEC towards the frequency of the waves. From simulations it is shown that also for real irregular waves, this tuning shows a significant beneficial effect on the absorbed energy. The control parameters for different wave conditions are discussed as well as the effect of the chosen PTO system and its constraints on the absorbed power and optimum control parameters

Development of two-variable maximum power point tracking control for ocean wave energy converters utilizing a power analysis and data acquisition system

Ocean wave energy shows great potential as a developing form of renewable energy. However, challenges arise in maturing this technology to achieve cost-effective energy conversion. Development and testing of wave energy converters can be problematic due to the harsh environment in which they are operated. To promote development of this technology, a platform is needed for comprehensive testing of these devices in this harsh environment. This will allow the determination of optimal topologies and control of wave energy converters for maximum power extraction. This work evaluates maximum power point tracking control in wave energy converters and presents two-variable maximum power point tracking control algorithms. A robust testing platform is developed for evaluating wave energy converters in the ocean environment. This testing platform is utilized in obtaining experimental data used in validating simulation results of the investigated control approaches

Short-Term Wave Forecasting with AR models in Real-Time Optimal Control of Wave Energy Converters

Time domain control of wave energy converters requires knowledge of future incident wave elevation in order to approach conditions for optimal energy extraction. Autoregressive models revealed to be a promising approach to the prediction of future values of the wave elevation only from its past history. Results on real wave observations from different ocean locations show that AR models allow to achieve very good predictions for more than one wave period in the future if the focus is put on low frequency components, which are the most interesting from a wave energy point of view. For real-time implementation, however, the lowpass filtering introduces an error in the wave time series, as well as a delay, and AR models need to be designed so to be as robust as possible to these errors

Short-Term Wave Forecasting with AR models in Real-Time Optimal Control of Wave Energy Converters

Time domain control of wave energy converters requires knowledge of future incident wave elevation in order to approach conditions for optimal energy extraction. Autoregressive models revealed to be a promising approach to the prediction of future values of the wave elevation only from its past history. Results on real wave observations from different ocean locations show that AR models allow to achieve very good predictions for more than one wave period in the future if the focus is put on low frequency components, which are the most interesting from a wave energy point of view. For real-time implementation, however, the lowpass filtering introduces an error in the wave time series, as well as a delay, and AR models need to be designed so to be as robust as possible to these errors

Feedback noncausal model predictive control of wave energy converters

In this paper, a novel feedback noncausal model predictive control (MPC) strategy for sea wave energy converters (WECs) is proposed, where the wave prediction information can be explicitly incorporated into the MPC strategy to improve the WEC control performance. The main novelties of the MPC strategy proposed in this paper include: (i) the recursive feasibility and robust constraints satisfaction are guaranteed without a significant increase in the computational burden; (ii) the information of short-term wave prediction is incorporated into the feedback noncausal MPC method to maximise the potential energy output; (iii) the sea condition for the WEC to safely operate in can be explicitly calculated. The proposed feedback noncausal MPC algorithm can also be extended to a wide class of control design problems, especially to the energy maximisation problems with constraints to be satisfied and subject to persistent but predictable disturbances. Numerical simulations are provided to show the efficacy of the proposed feedback noncausal MPC

Robust Tube-Based Model Predictive Control for Wave Energy Converters

This paper proposes an efficient robust tube-based model predictive control (RTMPC) strategy for energy-maximization control of wave energy converters (WECs) subjectto constraints due to safety considerations. Compared with the existing MPC strategies developed for the WEC control problem, the RTMPC method provides an effective approach to explicitly handle plant-model mismatches with guaranteed constraint satisfaction, contributing to improved energy capture efficiency. The fundamental idea is to integrate disturbance invariant sets into the MPC scheme for energy-maximization control to form a tube-based predictive controller, which enhances the robustness of MPC for a WEC without increasing online computational complexity. The resulting RTMPC controller can bound the WEC plant trajectories in a tube centered around a nominal WEC model trajectory, and uncertainties from un-modeled WEC dynamics and unmeasured disturbances can be mitigated by an error feedback portion. Numerical simulations demonstrate the effectiveness of the proposed control strategy

Robust optimization of control parameters for WEC arrays using stochastic methods

This work presents a new computational optimization framework for the robust control of parks of Wave Energy Converters (WEC) in irregular waves. The power of WEC parks is maximized with respect to the individual control damping and stiffness coefficients of each device. The results are robust with respect to the incident wave direction, which is treated as a random variable. Hydrodynamic properties are computed using the linear potential model, and the dynamics of the system is computed in the frequency domain. A slamming constraint is enforced to ensure that the results are physically realistic. We show that the stochastic optimization problem is well posed. Two optimization approaches for dealing with stochasticity are then considered: stochastic approximation and sample average approximation. The outcomes of the above mentioned methods in terms of accuracy and computational time are presented. The results of the optimization for complex and realistic array configurations of possible engineering interest are then discussed. Results of extensive numerical experiments demonstrate the efficiency of the proposed computational framework