Nonlinear model predictive control of a wave energy converter based on differential flatness parameterisation

This paper presents a fast constrained optimization approach, which is tailored for nonlinear model predictive control of wave energy converters (WEC). The advantage of this approach relies on its exploitation of the differential flatness of the WEC model. This can reduce the dimension of the resulting nonlinear programming problem (NLP) derived from the continuous constrained optimal control of WEC using pseudospectral method. The alleviation of computational burden using this approach helps to promote an economic implementation of nonlinear model predictive control strategy for WEC control problems. The method is applicable to nonlinear WEC models, nonconvex objective functions and nonlinear constraints, which are commonly encountered in WEC control problems. Numerical simulations demonstrate the efficacy of this approach

Model Predictive Control of a Wave Energy Converter with Discrete Fluid Power Power Take-Off System

Wave power extraction algorithms for wave energy converters are normally designed without taking system losses into account leading to suboptimal power extraction. In the current work, a model predictive power extraction algorithm is designed for a discretized power take of system. It is shown how the quantized nature of a discrete fluid power system may be included in a new model predictive control algorithm leading to a significant increase in the harvested power. A detailed investigation of the influence of the prediction horizon and the time step is reported. Furthermore, it is shown how the inclusion of a loss model may increase the energy output. Based on the presented results it is concluded that power extraction algorithms based on model predictive control principles are both feasible and favorable for use in a discrete fluid power power take-off system for point absorber wave energy converters

Influence of the excitation force estimator methodology within a predictive controller framework on the overall cost of energy minimisation of a wave energy converter

A large amount of energy is freely roaming around the world each day, without us being able to exploit it: wave energy is a largely untapped source of renewable energy, which can have a substantial influence in the future energy mix. The reason behind the inability of using this free resource is linked to the cost of the energy (CoE) produced from the different wave energy converters (WEC). The CoE from the different WECs is not yet comparable with other energy resources, due to a relative low efficiency coupled with the high structural costs. Within the sector a large effort has been addressed to optimize the WEC efficiency by means of different control strategies. In several articles [1, 2], it has been shown that with simple modifications of the control law, the absorbed energy can be doubled or quadrupled. Whilst the improvement of the efficiency will increase the revenue of the machine, the application of an advance control strategy will most probably increase the loads exerted on the structure, leading to an increment of the structural cost. Therefore, the problem of minimising the CoE produced by a WEC is at least a 2D problem. In a previous article [3], the minimisation problem has been investigated with a sequential approach, and the results have been reported for different control strategies. The Model Predictive Controller (MPC) seemed to have superior performance in terms of energy maximisation and loads on the structure, leading to a minimal CoE. But as presented in [3] the MPC was implemeted with perfect knowledge of the future load time series, which is physically not achivable. This article is an extension of the work presented in [3] with a closer focus on the influence of the excitation force prediction on the capability of the MPC architecture. Different estimator models of the excitation force time series are benchmarked, and validated with laboratory results

Towards real-time reinforcement learning control of a wave energy converter

The levellised cost of energy of wave energy converters (WECs) is not competitive with fossil fuel-powered stations yet. To improve the feasibility of wave energy, it is necessary to develop effective control strategies that maximise energy absorption in mild sea states, whilst limiting motions in high waves. Due to their model-based nature, state-of-the-art control schemes struggle to deal with model uncertainties, adapt to changes in the system dynamics with time, and provide real-time centralised control for large arrays of WECs. Here, an alternative solution is introduced to address these challenges, applying deep reinforcement learning (DRL) to the control of WECs for the first time. A DRL agent is initialised from data collected in multiple sea states under linear model predictive control in a linear simulation environment. The agent outperforms model predictive control for high wave heights and periods, but suffers close to the resonant period of the WEC. The computational cost at deployment time of DRL is also much lower by diverting the computational effort from deployment time to training. This provides confidence in the application of DRL to large arrays of WECs, enabling economies of scale. Additionally, model-free reinforcement learning can autonomously adapt to changes in the system dynamics, enabling fault-tolerant control

Power take-off simulation for scale model testing of wave energy converters

Small scale testing in controlled environments is a key stage in the development of potential wave energy conversion technology. Furthermore, it is well known that the physical design and operational quality of the power-take off (PTO) used on the small scale model can have vast effects on the tank testing results. Passive mechanical elements such as friction brakes and air dampers or oil filled dashpots are fraught with nonlinear behaviors such as static friction, temperature dependency, and backlash, the effects of which propagate into the wave energy converter (WEC) power production data, causing very high uncertainty in the extrapolation of the tank test results to the meaningful full ocean scale. The lack of quality in PTO simulators is an identified barrier to the development of WECs worldwide. A solution to this problem is to use actively controlled actuators for PTO simulation on small scale model wave energy converters. This can be done using force (or torque)-controlled feedback systems with suitable instrumentation, enabling the PTO to exert any desired time and/or state dependent reaction force. In this paper, two working experimental PTO simulators on two different wave energy converters are described. The first implementation is on a 1:25 scale self-reacting point absorber wave energy converter with optimum reactive control. The real-time control system, described in detail, is implemented in LabVIEW. The second implementation is on a 1:20 scale single body point absorber under model-predictive control, implemented with a real-time controller in MATLAB/Simulink. Details on the physical hardware, software, and feedback control methods, as well as results, are described for each PTO. Lastly, both sets of real-time control code are to be web-hosted, free for download, modified and used by other researchers and WEC developers

Application of Nonlinear Model Predictive Controller for Ocean Wave Energy Conversion Systems

This work addresses the application of Nonlinear Model Predictive Control (NMPC) to a class of ocean wave energy conversion systems in which the cost functional is not in a standard quadratic form, and the WEC model includes the nonlinear effects, such as the hydrodynamic viscous drag. The NMPC implementation is extended for MIMO WEC problems. Hybrid testing of the proposed method is performed using Linear Testbed (LTB) wave simulator at Wallace Energy Systems and Renewables Facility (WESRF) at Oregon State University. Simulations and experiments are conducted to verify the proposed methodology

Empowering wave energy with control technology: Possibilities and pitfalls

With an increasing focus on climate action and energy security, an appropriate mix of renewable energy technologies is imperative. Despite having considerable global potential, wave energy has still not reached a state of maturity or economic competitiveness to have made an impact. Challenges include the high capital and operational costs associated with deployment in the harsh ocean environment, so it is imperative that the full energy harnessing capacity of wave energy devices, and arrays of devices in farms, is realised. To this end, control technology has an important role to play in maximising power capture, while ensuring that physical system constraints are respected, and control actions do not adversely affect device lifetime. Within the gamut of control technology, a variety of tools can be brought to bear on the wave energy control problem, including various control strategies (optimal, robust, nonlinear, etc.), data-based model identification, estimation, and forecasting. However, the wave energy problem displays a number of unique features which challenge the traditional application of these techniques, while also presenting a number of control ‘paradoxes’. This review articulates the important control-related characteristics of the wave energy control problem, provides a survey of currently applied control and control-related techniques, and gives some perspectives on the outstanding challenges and future possibilities. The emerging area of control co-design, which is especially relevant to the relatively immature area of wave energy system design, is also covered