3 research outputs found
Control of wave energy converters using machine learning strategies
Wave energy converters are devices that are designed to extract power from ocean
waves. Existing wave energy converter technologies are not financially viable yet. Control
systems have been identified as one of the areas that can contribute the most
towards the increase in energy absorption and reduction of loads acting on the structure,
whilst incurring only minimal extra hardware costs. In this thesis, control schemes are
developed for wave energy converters, with the focus on single isolated devices.
Numerical models of increasing complexity are developed for the simulation of a point
absorber, which is a type of wave energy converter with small dimensions with respect
to the dominating wave length. After investigating state-of-the-art control schemes, the
existing control strategies reported in the literature have been found to rely on the model
of the system dynamics to determine the optimal control action. This is despite the fact
that modelling errors can negatively affect the performance of the device, particularly in
highly energetic waves when non-linear effects become more significant. Furthermore,
the controller should be adaptive so that changes in the system dynamics, e.g. due
to marine growth or non-critical subsystem failure, are accounted for. Hence, machine
learning approaches have been investigated as an alternative, with a focus on neural
networks and reinforcement learning for control applications. A time-averaged approach
will be employed for the development of the control schemes to enable a practical
implementation on WECs based on the standard in the industry at the moment.
Neural networks are applied to the active control of a point absorber. They are used
mainly for system identification, where the mean power is related to the current sea
state and parameters of the power take-off unit. The developed control scheme presents
a similar performance to optimal active control for the analysed simulations, which rely
on linear hydrodynamics.
Reinforcement learning is then applied to the passive and active control of a wave energy
converter for the first time. The successful development of different control schemes is
described in detail, focusing on the encountered challenges in the selection of states,
actions and reward function. The performance of reinforcement learning is assessed
against state-of-the-art control strategies. Reinforcement learning is shown to learn the
optimal behaviour in a reasonable time frame, whilst recognizing each sea state without
reliance on any models of the system dynamics. Additionally, the strategy is able to
deal with model non-linearities. Furthermore, it is shown that the control scheme is
able to adapt to changes in the device dynamics, as for instance due to marine growth