• Energy Research

In this paper, a generic coordinated control method for wave energy converters is proposed, and the constraints on motion amplitudes and the hydrodynamic interaction between converters are considered. The objective of the control problem is to maximize the energy converted from ocean waves, and this is achieved by coordinating the power take-off (PTO) damping of each wave energy converter in the frequency domain in each sea state. In a case study, a wave energy farm consisting of four converters based on the concept developed by Uppsala University is studied. In the solution, motion constraints, including constraints on the amplitudes of displacement and velocity, are included. Twelve months of sea states, based on measured wave data at the Lysekil test site on the Swedish west coast, are used in the simulation to evaluate the performance of the wave energy farm using the new method. Results from the new coordinated control method and traditional control method are compared, indicating that the coordinated control of wave energy converters is an effective way to improve the energy production of wave energy farm in harmonic waves.

An experimental campaign of arrays with direct-driven wave energy converters of point-absorbing type is presented. The arrays consist of six identical floats, moving in six degrees of freedom, and six rotating power take-off systems, based on the design developed at Uppsala University. The goals of the work were to study and compare the performances of three different array layouts under several regular and irregular long-crested waves, and in addition, to determine whether the numerical predictions of the best performing array layouts were confirmed by experimental data. The simulations were executed with a frequency domain model restricted to heave, which is a computationally fast approach that was merged into a genetic algorithm optimization routine and used to find optimal array configurations. The results show that good agreement between experiments and simulations is achieved when the test conditions do not induce phenomena of parametric resonance, slack line and wave breaking. Specific non-linear dynamics or extensive sway motion are not captured by the used model, and in such cases the simulation predictions are not accurate, but can nevertheless be used to get an estimate of the power output.

The total instantaneous energy transport can be found for polychromatic waves when using the deep water approximation. Expanding this theory to waves in waters of finite depth, we derive an expression for the total instantaneous energy transport for polychromatic fluid gravity waves based on potential theory with linearized free surface boundary conditions. We present the results for time series of wave elevation measured at the Uppsala University wave energy research test site. We show that a significant proportion of the total instantaneous energy transport is not accounted for when using the deep water theory. This is important since many wave energy conversion devices under development will operate in waters that do not fulfil the deep water criteria.

This paper introduces a machine learning based control strategy for energy converter arrays designed to work under realistic conditions where the optimal control parameter can not be obtained analytically. The control strategy neither relies on a mathematical model, nor does it need a priori information about the energy medium. Therefore several identical energy converters are arranged so that they are affected simultaneously by the energy medium. Each device uses a different control strategy, of which at least one has to be the machine learning approach presented in this paper. During operation all energy converters record the absorbed power and control output; the machine learning device gets the data from the converter with the highest power absorption and so learns the best performing control strategy for each situation. Consequently, the overall network has a better overall performance than each individual strategy. This concept is evaluated for wave energy converters (WECs) with numerical simulations and experiments with physical scale models in a wave tank. In the first of two numerical simulations, the learnable WEC works in an array with four WECs applying a constant damping factor. In the second simulation, two learnable WECs were learning with each other. It showed that in the first test the WEC was able to absorb as much as the best constant damping WEC, while in the second run it could absorb even slightly more. During the physical model test, the ANN showed its ability to select the better of two possible damping coefficients based on real world input data.

A challenge while applying latching control on a wave energy converter (WEC) is to find a reliable and robust control strategy working in irregular waves and handling the non-ideal behavior of real WECs. In this paper, a robust and model-free collaborative learning approach for latchable WECs in an array is presented. A machine learning algorithm with a shallow artificial neural network (ANN) is used to find optimal latching times. The applied strategy is compared to a latching time that is linearly correlated with the mean wave period: It is remarkable that the ANN-based WEC achieved a similar power absorption as the WEC applying a linear latching time, by applying only two different latching times. The strategy was tested in a numerical simulation, where for some sea states it absorbed more than twice the power compared to the uncontrolled WEC and over 30% more power than a WEC with constant latching. In wave tank tests with a 1:10 physical scale model the advantage decreased to +3% compared to the best tested constant latching WEC, which is explained by the lower advantage of the latching strategy caused by the non-ideal latching of the physical power take-off model.

Potential theory is used to derive closed-form expressions for the energy flux in propagating polychromatic surface gravity waves for water of infinite depth. The energy flux vector has been obtained as convolution-type integrals of the wave-elevation time series at one point on the surface. An expression for the integrated flux through a vertical surface is also given. It is shown that the integrated flux cannot be negative at any instant in time.

The ocean are largely an untapped source of energy. However, compared to other energies, power fluctuations for ocean waves are small over longer periods of time. This paper present a grid-oriented approach to electricity production from ocean waves, utilizing a minimal amount of mechanical components.

For most wave energy technology concepts, large-scale electricity production and cost-efficiency require that the devices are installed together in parks. The hydrodynamical interactions between the devices will affect the total performance of the park, and the optimization of the park layout and other park design parameters is a topic of active research. Most studies have considered wave energy parks in long-crested, unidirectional waves. However, real ocean waves can be short-crested, with waves propagating simultaneously in several directions, and some studies have indicated that the wave energy park performance might change in short-crested waves. Here, theory for short-crested waves is integrated in an analytical multiple scattering method, and used to evaluate wave energy park performance in irregular, short-crested waves with different number of wave directions and directional spreading parameters. The results show that the energy absorption is comparable to the situation in long-crested waves, but that the power fluctuations are significantly lower.