Wave Energy Converter Research Articles

Optimization of Design Parameters for Improved Buoy Reliability in Wave Energy Converter Systems

Wave energy converters are frequently subjected to cyclic fatigue loads, making them prone to structural failure. This study presents a comprehensive design for reliability analysis of buoy structures used in ocean energy converters. A finite element model (FEM) was developed using ABAQUS to evaluate the effects of different materials—linear low-density polyethylene (LLDPE) versus high-density polyethylene (HDPE)—as well as variations in rib spacing and structural thickness under uniform pressure conditions. The analysis considered configurations with 3, 5, and 7 ribs, and wall thicknesses of 0.5, 0.7, and 1 inch. Results indicated that increasing the number of ribs and wall thickness significantly reduces deflection and von Mises stress, enhancing structural stability. HDPE demonstrated superior strength and lower deflection compared to LLDPE, although with reduced ductility. This study provides critical insights into optimizing buoy design parameters to improve the structural performance and durability of wave energy converter buoys, ensuring their reliability and longevity in harsh marine environments.

A wave forecasting method based on probabilistic diffusion LSTM network for model predictive control of wave energy converters

Accurate long-term prediction of wave excitation forces is critical for optimizing wave energy converters, enabling enhanced control, and improving energy absorption efficiency. Traditional prediction models often employ deterministic conservative approaches, overlooking uncertainties. This paper introduces a novel prediction method based on the probabilistic diffusion model, offering a more precise prediction while accounting for uncertainty. Notably, our approach goes beyond previous works by simultaneously achieving control objectives for maximizing energy absorption, contrasting with methodologies solely focused on prediction and control tasks. The proposed method utilizes long short-time memory to extract temporal information from historical wave excitation observation. The hidden features are then processed through a diffused probabilistic-based unit. Subsequently, a time-scale neural network is developed for wave excitation moment prediction, followed by the application of nonlinear model predictive control to maximize the energy absorption. The methodology is validated on the 1:20 Wavestar prototype devices through numerical simulations. Results indicate that the predicted excitation moment closely align with measured values. In cases 4, 5, 6, the proposed method yields a substantial improvement in maximum control energy absorption–22%, 16%, and 31%, respectively–compared to traditional prediction methods. The proposed approach not only achieves a more accurate wave excitation moment prediction with uncertainty consideration but also introduces advanced control strategies in a full-scale plant application. This work contributes significantly to the field by bridging the gap between precise prediction and effective control in wave energy conversion systems.

Robustness of Structural Health Monitoring Systems for Offshore Energy Structures based on Damage Characteristics

Offshore energy structures such as fixed/floating wind turbines, tidal turbines and wave energy converters are exposed to loads coming from different sources. Recent advances in SHM offer many unique opportunities to assess the structural integrity of offshore energy infrastructure. The process of SHM generally involves the use of a system of sensors mounted on a structure; the processing of signals received from the sensors to attain damage sensitive features; and the detection of damage in the structure. Over the past decades, most of the research in SHM has been focused on improving and developing new sensor technology, signal processing, and damage detection algorithms. However, there are very few studies assessing the performance of SHM systems with respect to damage detection, localization, and quantification. Moreover, most of the performance analysis models for SHM systems rely only on 'probability of detection (POD)', that is an assessment index used in NDT to evaluate the probability of detecting a damage as a function of its size. For SHM systems, the replicability of POD is influenced by the environment, measurement noise, and the deterioration of sensors and instruments. Therefore, the distribution of POD is often determined by performing an extensive number of test sets with existing damage of varying magnitude. This paper aims to reduce the volume of experimental data required, as well as the uncertainties associated with POD prediction by using a model-assisted probability of detection (MAPOD) approach. We develop a Bayesian network (BN) method to evaluate the performance of SHM systems in terms of damage detection, localization, and assessment. In addition to POD, the probability of accurate localization (POAL) and probability of accurate assessment (POAA) are also incorporated into the analysis. The BN and limit state functions are applied to a fixed offshore energy structure equipped with a vibration based SHM system.

Enhancement of Energy Harvesting PTO Against Nonlinear Waves Using Control System

Abstract Many systems include turbines, linear electromagnetic or hydroelectric components, as well as wave energy converters designed particularly for energy harvesting from ocean and sea waves. While each of these methods has its merits, it is also feasible to harvest a considerable quantity of energy generated by the sea and ocean waves. Wave energy is considered a renewable and ecologically friendly energy source. It is harvested through wave energy converters and is particularly useful to coastal nations. In this paper, a mechanical motion rectifier (MMR) is proposed to convert the bidirectional reciprocating movement of a floating body above the nonlinear wave into a rotating movement in one direction. Experimental investigations are carried out to examine the performance of the system. An experimental wave tank was built to produce nonlinear waves by controlled flapper motion. Mechanical and electrical efficiency were investigated for both systems. Through the use of a generator, the energy can be taken in the form of electrical energy and stored in appropriate batteries, resulting in clean and sustainable energy from the sea’s waves.

Optimization of latching control for duck wave energy converter based on deep reinforcement learning

In the field of wave energy extraction, employing active control strategies amplifies the Wave Energy Converter's (WEC) response to wave motion. In this regard, a numerical simulation study was conducted on the interaction between Duck WEC and waves under discrete latching control, proposing an optimized latching duration strategy based on Deep Reinforcement Learning (DRL). The study utilized an improved Edinburgh Duck model to establish a numerical wave flume (NWF), verifying the motion of waves and the device. A DRL and Computational Fluid Dynamics (CFD) coupled framework was developed to implement the latching control strategy. In the limited trichromatic wave testing conditions provided in this paper, the latching control time is optimized using a DRL agent, and its capture efficiency is compared with two non-predictive latching control strategies. The DRL agent control showed a 13.4% improvement relative to reference threshold control, with less than 6.3% differences compared to optimal threshold latching results. Improvement of approximately 5.6% was observed compared to optimal constant latching control. For the first time in nonlinear fluid dynamics numerical simulation, this study demonstrates the efficacy of model-free reinforcement learning with discrete latching actions, establishing an environment-driven control strategy, and having the potential to become a general strategy for WEC latching control.

A Sea-State-Dependent Control Strategy for Wave Energy Converters: Power Limiting in Large Wave Conditions and Energy Maximising in Moderate Wave Conditions

A Sea-State-Dependent Control Strategy for Wave Energy Converters: Power Limiting in Large Wave Conditions and Energy Maximising in Moderate Wave Conditions

Robust Learning-Based Model Predictive Control for Wave Energy Converters

Robust Learning-Based Model Predictive Control for Wave Energy Converters

An Experimental Study on the Effects of Perforated Floating Structures and Submerged Plates for Wave Control and Motion Reduction of Pile-Moored Floating Piers

The floating pier is a representative type of floating structure installed along the coast, primarily used as a facility for berthing and mooring ships. Additionally, ongoing attempts have been made to utilize it for various purposes, such as wave control and wave energy conversion structures. In this study, we experimentally investigated the reflection and motion characteristics of a pile-moored floating pier, which allows heave and limited roll motion, with respect to the presence of perforated structures and the attachment of submerged plates. The hydraulic experiment results indicated that the reflection and motion characteristics of the pile-moored floating pier were significantly influenced by the presence and installation depth of the submerged plates, rather than the presence of perforated structures on the floating body. In particular, the installation of submerged plates increased the reflection coefficient in short-period waves and effectively reduced the heave and roll motions of the floating body.

Optimization of Submerged Breakwaters for Maximum Power of a Point-Absorber Wave Energy Converter Using Bragg Resonance

This study focused on optimizing the power generation of a heaving point-absorber wave energy converter (HPA-WEC) by integrating submerged breakwaters. An optimization analysis was conducted based on a framework developed in the authors’ previous work, aiming to maximize the capture width ratio (CWR) by inducing Bragg resonance. Numerical simulations were conducted using a two-dimensional frequency domain boundary element method (FD-BEM) under irregular wave conditions. Advanced particle swarm optimization (PSO) was used for the optimization, with design variables that included the power take-off (PTO) damping coefficient, spring constant, and position and shape of the submerged breakwaters. The results showed that the CWR almost doubled when two breakwaters were used compared with the case without breakwaters. The CWR significantly increased, even with only one breakwater installed behind the WEC. A coastal stability analysis showed that installing two breakwaters provided the best performance, reducing the transmitted wave energy by approximately 25%. Furthermore, the CWR reached its maximum when the distance between the breakwater endpoints equaled the wavelength of the peak wave frequency, indicating the occurrence of Bragg resonance. This study underscores the potential of submerged breakwaters in enhancing power generation and coastal stability in the design of HPA-WECs.

An integrated approach for the decision of wave energy converter deployment based on forty-five-years high-resolution wave climate modeling

Climate change is driving the urgent need for sustainable and renewable energy sources to mitigate its impacts and reduce greenhouse gas emissions. Ocean wave energy, as one of the promising alternatives to fossil fuels, plays a critical role in providing renewable energy in coastal areas. To fully exploit regional wave energy potential, various performance metrics of wave energy converters (WECs) need to be considered to optimize location decisions and WEC categories. This study introduces an integrated approach by incorporating a novel factor, the maximum consecutive cut-off duration of WECs due to extreme weather conditions. It aims to enhance the efficiency of WEC selection and deployment location optimization in the southern and eastern Asian seas. Through 45 years of high-resolution (up to 250 m) ocean wave simulations, conducted using WaveWatch III ®(WW3) on unstructured mesh, Wave Dragon was identified as providing the highest index. This study suggests that placing WECs along the coastlines of the South China Sea, the eastern and northern Bay of Bengal, the western sector of the Malay Archipelago, and parts of the Arafura Sea may result in the most efficient conversion of wave energy in the southern and eastern Asian seas. While the proposed approach alone may not dictate deployment decisions, this study provides an applicable index to potentially rank WEC deployment options in the eastern and southern Asian seas, by considering both exploitable wave energy production, and long-term stability.

AN EVALUATION OF WAVE ENERGY GENERATION AND COST OF ENERGY IN THE BLACK SEA

The performance of several axisymmetric wave energy converters is studied by evaluating the yearly energy capture and the expense of energy in two sites in the Black Sea. The added mass, hydrodynamic damping, and wave forces exerted on the floats are calculated by a 3D panel method based on potential flow theory. The oscillations of the floats are calculated in the time domain by employing a family of Runge-Kutta Methods at various levels of accuracy and the yearly energy generated is calculated by taking into account the occurrence of sea states in a year. The expense of energy captured by each wave energy converter is evaluated by calculating the Levelized Cost of Energy. The results show that the WECs with Berkeley Wedge-Shaped floats generate the maximum amount of energy in Sinop and Hopa. The most economical wave energy converters are those with a cone float and with a Berkeley Wedge-Shaped float in Sinop and Hopa, respectively.

Assessment of a Hybrid Wind–Wave Energy Converter System in Nearshore Deployment

Assessment of a Hybrid Wind–Wave Energy Converter System in Nearshore Deployment

Wave power calculation of a large periodic array of bottom-hinged paddle wave energy converters using Floquet–Bloch theory

In this study we consider a wave energy farm consisting of a large number of bottom-hinged paddles. The paddles are arranged in a doubly-periodic manner, with a finite number of identical rows and each row containing an infinite number of paddles. Each paddle is attached to its own damper and spring, allowing power to be generated through its pitching motion. Unlike previous studies into paddle arrays, we envisage compact arrays consisting of small devices arranged across a large number of rows. The mathematical analysis of this proposed configuration is best suited to methods that exploit the periodicity of the array. It is shown that the velocity potential everywhere within the array can be described by an expansion in terms of suitably-defined Floquet–Bloch eigenmodes and this allows the solution to the scattering problem involving waves incident upon the array to be determined by a simple low-order system of equations. The method used in this study is exact within the setting of linearised water waves and has all of the advantages of classical low-frequency multi-scale homogenisation without the low-frequency restriction. It may also be regarded as a natural extension of the well-known wide-spacing approximation without the large separation restriction. Although the focus of this paper is on the mathematical approach to the solution of the problem, we provide a range of results to illustrate the performance of this proposed wave energy converter concept.

The potential of Wave Energy Converters in the Galapagos islands

This study investigates the potential of Wave Energy Converters (WECs) technology in the Galapagos Islands. Despite the region’s high wave energy potential (262 TWh/year of gross potential and 25TWh/year of sustainable potential), the current high cost of WEC technology limits its economic competitiveness compared to mature renewables like solar and wind. Our analysis, using the OSeMOSYS model, reveals that decarbonizing the Galapagos’ power sector with solar and wind is feasible and cost-effective. However, deploying WECs is even more expensive. Initially, WECs have the highest levelized cost of electricity (LCOE) (43 ct.USD/kWh in 2030), but this decreases by 2050, ending up at 26 ct.USD/kWh, a value below the benchmark for fossil thermal power plants without fuel subsidies. Deploying WECs, together with solar and wind technologies may contribute for diversifying the Galapagos’ power system, reducing fossil fuel reliance, and mitigating climate change impacts.

Power absorption and dynamic response analysis of a hybrid system with a semi-submersible wind turbine and a Salter's duck wave energy converter array

A multi-energy system that integrates a floating offshore wind turbine (FOWT) with wave energy converters (WEC) is an effective way to commercialize new offshore energy and the levelized cost of energy in the future. This paper proposes a FOWT-WEC hybrid system concept consisting of a semi-submersible FOWT with a Salter's duck (SD) WEC array. A coupling framework is established based on OpenFAST and ANSYS-AQWA. A preliminary feasibility study is conducted on the power absorption and motion response of the hybrid system to evaluate the interaction between the SD WEC array and the FOWT. Systematic studies demonstrate that the SD WEC array significantly reduces the motion response of the hybrid system in terms of pitch and heave. An increase in the equilibrium position of the platform in the surge direction does not have a large effect on the stability of the system. The WEC array improves the stability of wind power output and can be used as an effective supplement to power generation. The effect of wave direction shows that the SD WEC has a high sensitivity to the angle of incident waves, and the diffraction and radiation from the platform have a large impact on the power absorption of the SD WEC array.

Geometry optimization of a floating platform with an integrated system of wave energy converters using a genetic algorithm

This study uses a genetic algorithm(GA) to investigate the practicality of optimizing the geometry and dimensions of a floating platform, which houses pitching wave energy converters (WEC). Using frequency-domain analysis, sensitivity tests for the search start point, choice of optimized variable, number of iterations, simulation time, and contents of the search space are made. Results show that the required number of iterations to convergence increases with an increased number of optimized variables. Furthermore, for the studied platform geometry, no single global optimum exists. Instead, various combinations of characteristic features can lead to comparable performances of the integrated wave absorber. Finally, it is observed that when the solution space is controlled and made to contain a subset of potential solutions known to improve the system performance, computation time, absorption efficiency and range are observed to improve. Additionally, the GA optimum tends towards platform geometries for which the wave absorber’s resonance response corresponds to the dominating wave climate frequencies. A key contribution of this study is the controlled manipulation of the solution space to contain a subset of potential solutions that enhance system performance. This controlled approach leads to improvements in computation time, absorption efficiency, and range of the system.

Hybrid Torque Coefficient Control of Average-to-Peak Ratio for Turbine Angular Velocity Reduction in Oscillating-Water-Column-Type Wave Energy Converter

Wave energy converters (WECs) have significant potential to meet the increasing energy demands and using an oscillating water column (OWC) is one of the most reliable ways to implement them. The OWC has a simple structure and excellent durability. However, control of the power take-off (PTO) system is difficult due to variability in the input wave energy. In particular, the design and control of the PTO system are complex, as the average-to-peak ratio of the output generation is large. Owing to the nature of the OWC, if the energy above the rating cannot be controlled, the power generated is inevitably reduced due to the decrease in operating time. We propose a method to reduce the angular speed of the turbine by dividing the section according to the input energy and correspondingly changing the torque coefficient, thereby increasing the operating time of the OWC. The control methods for the PTO system of OWC are verified through a 30 kW full-scale experimental device to be installed in a real sea area. The full-scale experimental device consists of an inverter that simulates the mechanical torque of an OWC based on the aerodynamic simulation of an impulse turbine, an induction motor, a permanent magnet synchronous generator, an AC/DC converter, and a battery for the energy storage system. The performance of conventional control methods and the proposed method are compared based on the results of numerical simulations and experiments. We show that the fluctuation in the turbine angular velocity in the proposed method is significantly reduced compared with that in the conventional control methods under regular and irregular wave conditions.

Reliability modeling of different wave energy conversion technologies

Reliability modeling of different wave energy conversion technologies

Experimental study on the hydrodynamic performance of a multi-DOF WEC-type floating breakwater

A hybrid system consisting of wave energy converters and breakwaters can be applied for both wave energy conversion and shoreline protection. Besides, the cost of wave energy conversion is believed to be reduced by such a combination. In this paper, a two-dimensional asymmetric wave energy converter-type breakwater with a triangle-baffle bottom that can convert wave energy through its heave motion is proposed and investigated experimentally in regular waves. A supporting system is designed and established to allow the model to move in a single degree of freedom or prescribed combination of multiple-degree-of-freedoms. The effects of the geometric asymmetry, the bottom slope, the width-to-draft ratio, and the multiple-degree-of-freedom motion on the wave energy conversion and attenuation performances are analyzed. The results show that the triangle-baffle wave energy converter-type breakwater has much higher wave energy conversion efficiency than its rectangular counterpart with the same displacement. Increasing the bottom slope and allowing pitch motion can improve efficiency, allowing surge motion will reduce efficiency, and the effect of increasing the width is uncertain due to two conflicting effects. The asymmetry, bottom slope, and width only have a limited influence on the wave attenuation, whereas surge and pitch motions reduce the wave attenuation in short waves, analogizing a wavemaker.

[formula omitted][formula omitted]ave [formula omitted]earner: Predicting wave farms power output using effective meta-learner deep gradient boosting model: A case study from Australian coasts

Precise prediction of wave energy is indispensable and holds immense promise as ocean waves have a power capacity of 30–40 kW/m along the coast. Utilising this energy source does not generate harmful emissions, making it a superior substitute for fossil fuel-based energy. The computational expense associated with simulating and computing intricate hydrodynamic interactions in wave farms restricts optimisation methods to a few thousand evaluations and makes a challenging situation for training in deep neural prediction models. To address this issue, we propose a new solution: a Meta-learner gradient boosting method that employs four multi-layer convolutional dense neural network surrogate models combined with an optimised extreme gradient boosting. In order to train and validate the predictive model, we used four wave farm datasets, including the absorbed power outputs and 2D coordinates of wave energy converters (WECs) located along the southern coast of Australia, Adelaide, Sydney, Perth and Tasmania. Furthermore, the capability of the transfer learning strategy is evaluated. The WECs used in this study are of the fully submerged three-tether converter type, similar to the CETO prototype. The effectiveness of the proposed approach is assessed by comparing it with 15 well-established and effective machine learning (ML) methods. The experimental findings indicate that the proposed model is competitive with other ML and deep learning approaches, exhibiting considerable accuracy of 88.8%, 90.0%, 90.3%, and 84.4% in Adelaide, Perth, Sydney and Tasmania and improved robustness in predicting wave farm power output.