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To tune the wave energy converter (WEC) controller parameters such as damping to reduce the line force during extreme wave conditions, future knowledge of the line force is required.To achieve this, the incoming wave and system state should be predicted for a few seconds in the future. It is rather an arduous task to predict the future knowledge of waves and the system's dynamic when dealing with breaking and steep waves, and the system is subject to various nonlinear forces. The classical model-based control strategies often rely on linear assumptions to estimate the WEC dynamics for the sake of simplicity. Unlike the model-based, the data-driven approaches are free from modeling errors and the algorithms are trained over the true and noisy data to predict non-linear system behaviors.Using data-driven approaches, we are able to model nonlinear dynamics. However, new questions emerge on the accuracy of the future wave and system state predictions, and how this uncertainty propagates into the final prediction of the line force. As incorrect damping may lead to excessive line force and detrimental damage to the system, these are the knowledge gaps that need to be addressed.The main purpose of this paper is to answer these questions through a survivability strategy for wave energy converters by providing a realistic perspective on the implementation of the neural network approaches by accounting for the errors in the input data. For this purpose, a series of neural networks is designed that first predicts the surface elevation for 0.36 s ahead, i.e. corresponding to 2 s in the full-scale WEC. This future knowledge of the wave elevation is then used to predict the system state (i.e. power take-off (PTO) translator position) for the same prediction horizon based on the PTO damping. This information is then fed to a convolutional neural network (CNN) that predicts the peak line force 0.36 s ahead. Further, the sensitivity of the peak line force prediction to the uncertainties in the input data and the prediction horizon is analyzed. The neural network models are trained over the experimental data subjected to the extreme sea states for a point absorber wave energy converter. The results present a thorough analysis of the NN models’ performance.The results suggest that the accuracy of the surface elevation prediction has an insignificant independent effect on the peak force prediction model. However, these uncertainties reflect in the PTO translator position prediction, and the model is considerably sensitive to the accuracy of this prediction. This sensitivity nonetheless is less notable for higher PTO damping values. The prediction accuracy of the peak forces dropped by only about 7\% when the predicted input was used in the lower damping cases here, whereas, a larger drop was seen for the higher damping case.  

One strategy for the survivability of wave energy converters(WECs) is to minimize the extreme forces on the structure by adjusting the system damping. In this paper, a neural network model is developed to predict the peak line force for a 1:30 scaled point-absorber WEC with a linear friction-damping power take-off (PTO). The algorithm trains over the wave tank experimental data and enables an update of the system damping based on the system state (i.e. position, velocity, and acceleration) and information on the incoming waves for the extreme sea states. The results show that the deep neural network (DNN) developed here is relatively fast and able to predict the peak line forces with a correlation of 88% when compared to the true (experimental)data. Then, the optimum damping for survivability purposes is found by minimizing the peak line force. It is shown that the optimum damping varies depending on the system state in each zero up-crossing episode.

The survivability of wave energy converters (WECs) is one of the challenges that have a direct influence on their cost. To protect the WEC from the impact of extreme waves, it is often to over-dimension the components or adopt survivability modes e.g. by submerging or lifting the WEC if it is applicable. Here, a control strategy for adjusting the system damping is developed based on deep neural networks (DNN) to minimize the line (mooring) force exerted on a 1:30 scaled WEC. This DNN model is then implemented in a control system of a numerical WEC-Sim model to find the optimal power take-off (PTO) damping for every zero up-crossing episode of surface elevation which minimizes the peak line force. The WEC-Sim model was calibrated based on a 1:30 scaled wave tank experiment that was designed to investigate the WEC response in extreme sea states with a 50-year return period. It is shown that this survival strategy reduces the peak forces when compared with the response of a system that has been set to a constant PTO damping for the entire duration of the sea state.

Predicting the response of point absorber wave energy converters (WECs) in extreme sea states is crucial for assessing their survivability. However, data are scarce and hydrodynamic understanding is limited. In order to simulate extreme wave conditions, laboratory-scale focused waves based on NewWave theory have been utilized. To investigate the interactions between focused waves and a point absorber WEC, a wave basin experiment has been conducted. Various parameters, including focusing amplitude and peak frequency have been examined across three different damping conditions. The motion response of the point absorber WEC and the corresponding mooring force have been measured over time. The experimental findings reveal that both the focused wave parameters and the damping values have a significant influence on the motion response and mooring force. It is shown that an increase in the focusing amplitude leads to a more intense motion response, while the mooring force is relatively insensitive to the focused amplitude/peak frequency when the end-stop spring is not compressed. The force in the connection line is maximized when the upper end-stop spring is compressed. As the peak frequency increases, the heave and surge responses decrease, whereas the maximum mooring force increases with peak frequency for a locked power take-off (PTO) system. Finally, the results indicate that optimizing the design of the power take-off system, including selecting appropriate damping values and stroke lengths for the translator, can significantly reduce the mooring load for extreme wave conditions.

To determine the optimal design of the wave energy converter (WEC) that can withstand extreme waveconditions, the short- and long-term extreme responses of the system need to be determined. This paper focuses on the extreme peak force distribution of the mooring force for a 1:30 scaled point absorber WEC. The basis of this analysis is the mooring force response obtained from a WEC-Sim model calibrated by wave tank experimental data. The extreme sea states have been chosen from a50-year environmental contour. Here, first, the long-term extreme response using the full sea state approach is obtained for three constant damping cases of the power take-off (PTO) system. Then, using a contour approach, the expected value of the extreme peak line (mooring) force distribution is computed for the sea states along an environmental contour. Further, for the most extremesea state, the extreme peak line force distribution is also computed where a survivability control system, based on a deep neural network (DNN), changes the PTO damping to minimize the peak mooring force in each zero up-crossing episode of surface elevation. The results show that in the absence of a control system, the zero PTO damping case is a conservative choice in the analysis of the long-term response and the design load. For the most extreme sea state along the environmental contour, the survivability control system slightly reduces the expected value of the extreme peak force distribution when compared with lower constant PTO damping configurations.

Wave energy conversion is a renewable energy technology with a promising potential. Although it has been developed for more than 200 years, the technology is still far from mature. The survivability in extreme weather conditions is a key parameter halting its development. We present here results from two weeks of measurement with a force measurement buoy deployed at Uppsala University’s test site for wave energy research at the west coast of Sweden. The collected data have been used to investigate the reliability for two typical numerical wave energy converter models: one low fidelity model based on linear wave theory and one high fidelity Reynolds-Averaged Navier–Stokes model. The line force data is also analysed by extreme value theory using the peak-over-threshold method to study the statistical distribution of extreme forces and to predict the return period. The high fidelity model shows rather good agreement for the smaller waves, but overestimates the forces for larger waves, which can be attributed to uncertainties related to field measurements and numerical modelling uncertainties. The peak-over-threshold method gives a rather satisfying result for this data set. A significant deviation is observed in the measured force for sea states with the same significant wave height. This indicates that it will be difficult to calculate the force based on the significant wave height only, which points out the importance of more offshore experiments.

Emerging offshore renewable energy technologies are expected to become an important part of the futureenergy system, and reliability for these new technologies in different metocean scenarios must be guaranteed.This poses a challenge in extreme weather scenarios like storms, in particular for less mature technologiessuch as wave energy. Not only the offshore survivability must be controlled; the restoration after disruptiveevents and failures should be addressed and optimized. Offshore operations are costly and cannot be carriedout if the weather is too harsh, and the resulting downtime after failures may be financially devastating forprojects. In this paper, the resilience of large wave energy systems is studied with respect to wave conditions,metocean dependent failure rates, and weather windows available for offshore repair operations. A metocean-and time-dependent failure rate is derived based on a Weibull distribution, which is a novelty of the paper.The performance of the farm is assessed using the varying failure rates and metocean data at different offshoresites. Critical metocean thresholds for different offshore vessels are considered, and the resilience is quantifiedusing relevant measures such as unavailability and expected energy not supplied. The resilience analysis iscoupled to an economic assessment of the wave farm and different repair strategies. Our results show thatthe commonly used assumption of constant failure rates is seen to overestimate the annual energy productionthan when a more realistic varying failure rate is used. Two offshore sites are compared, and the availabilityis found to be higher at the calmer site. Most of the evaluated repair strategies cannot be considered to beeconomically justified, when compared to the cost of the energy not supplied.

The design of wave energy converters should rely on numerical models that are able to estimate accurately the dynamics and loads in extreme wave conditions. A high-fidelity CFD model of a 1:30 scale point-absorber is developed and validated on experimental data. This work constitutes beyond the state-of-the-art validation study as the system is subjected to 50-year return period waves. Additionally, a new methodology that addresses the well-known challenge in CFD codes of mesh deformation is successfully applied and validated. The CFD model is evaluated in different conditions: wave-only, free decay, and wave–structure interaction. The results show that the extreme waves and the experimental setup of the wave energy converter are simulated within an accuracy of 2%. The developed high-fidelity model is able to capture the motion of the system and the force in the mooring line under extreme waves with satisfactory accuracy. The deviation between the numerical and corresponding experimental RAOs is lower than 7% for waves with smaller steepness. In higher waves, the deviation increases up to 10% due to the inevitable wave reflections and complex dynamics. The pitch motion presents a larger deviation, however, the pitch is of secondary importance for a point-absorber wave energy converter.

To ensure a reliable operation over the life time of wave energy converters (WECs), a number of load cases need to be considered according to international standards for marine structures to determine an optimal design. This paper outlines the procedure of obtaining an environmental design load for the line force of a 1:30 scaled point-absorber WEC using an environmental contour with a 50-year return period for the Dowsing site in the North Sea. To obtain the response of the WEC during extreme conditions, a numerical WEC-Sim model is developed and calibrated with experimental wave tank tests to augment the data required for such design load analysis. The design load for the line force is estimated based on the full long-term extreme response computed from the full sea state approach by considering 180, 360, and 720 sea state samples as well as the contour approach for the sea state that gives the most extreme response. Further, a probabilistic approach is used to quantify the probability of failure for a critical mechanical component of the system such as shackle. The result shows that the numerical WEC-Sim model is able to sufficiently replicate the real response of the system during extreme irregular waves. The Bayesian theory with Monte-Carlo algorithm is found to be an excellent tool for identifying the degree of belief in the statistical models used for the short-term extreme response analysis. Considering the ultimate limit state, the design load for the 1:30 scaled system is calculated as 670.95 N (i.e. 18.11 MN for a full-scale system) after applying the partial load safety factor of 1.35 on the full long-term extreme response of the system for the 9.1 years return period (i.e. 50 years in a full-scale model).

The experimental results of a 1:30 scaled wave tank experiment of a point-absorber in extreme sea states and intermediate water depth are studied. The effect of the PTO damping parameter and different non-linear phenomena in extreme wave conditions such as wave breaking and overtopping are investigated with the focus on the maximum line (mooring) force in the presence of an upper end-stop. In the comparison of different wave-type representations, i.e. irregular, regular, and focused waves, of the same sea state, not all the wave types necessarily yield to the same peak line force. Moreover, there exists an optimum damping value for each sea state in which the smallest peak force is achieved. Both end-stop spring compression and wave-breaking slamming result in peak line forces which may be compensated by overtopping water pressure. Large surge motion is obtained for waves with a long wavelength which can contribute to higher and more damaging line forces.