Archimedes Wave Swing Research Articles

A study of appropriate wave energy technology for sustainable development in Australia

The deployment and development of wave energy systems to increase energy sector efficiency is essential for governments. To develop and maximize the exploitation of wave energy sources, applying appropriate technologies is extremely important. The design of decision-making tools to identify the best technology for developing energy resources optimally is one of the primary decision-making issues in the energy sector. In this article, we have proposed a Multi-Criteria Decision Making (MCDM) model to select suitable technologies among eleven wave energy harvesting technologies: OPT PowerBuoy, AquaBuoy, Archimedes Wave Swing, Salter’s Duck, Aquamarine PowerOyster, Bio Wave, SEAREV, Weptos, Mighty Whale, Wave dragon. To handle conflicting objectives during evaluation, the Fuzzy AHP method is used to calculate the weight of the criteria. Then, wave energy harvesting technology is ranked using the Fuzzy TOPSIS approach. An actual case study from Australia was examined in order to show the viability of the suggested decision-making methodology. This study applies a valuable reference to the issue of wave technology selection; Therefore, managers involved in wave energy can use the problem-solving approach in this study to identify the most suitable wave energy technology based on their criteria. Research results show that the most suitable technologies for optimal development of wave energy sources in Australia are WET-05 and WET-03 with coefficients of 0.852 and 0.806, respectively. Meanwhile, two technologies considered unsuitable for developing wave energy sources in this study are WET-06 and WET-11 with scores of 0.375 and 0.381, respectively. In this study, the combined application of the two FAHP-FTOPSIS methods is considered more appropriate because of its theoretical ease of understanding as well as the simplicity of application and robustness of the results.

Wave energy converter farm feasibility assessment in southwest Baja California, Mexico

This study evaluates the technical and economic feasibility of implementing a wave energy converter (WEC) farm on the southwest coast of Baja California, Mexico. The aim is to determine the theoretical installed capacity and the levelized cost of energy (LCOE) for a WEC farm, unlike most previous efforts that were limited to a single device. The availability of wave energy has been assessed using 40 years of wave data. The electrical power generation potential of the Archimedes Wave Swing and Wave Dragon devices was estimated using their power matrices. For the calculation of levelized cost of energy, the capital expenditure includes equity, debt, taxes, and the present value of the power generation over the project's lifespan. The results indicate that the Archimedes Wave Swing farms offer the most favourable pricing for end users, despite the Wave Dragon farms having a larger installed capacity. A sensitivity analysis was conducted for five variables, which revealed that site selection has the greatest impact on LCOE.

Dynamic performance enhancement of nonlinear AWS wave energy systems based on optimal super-twisting control strategy

This paper presents a Super-Twisting Algorithm (STA) sliding mode control technique as an alternative to the conventional Proportional-Integral (PI) control system. This application is specifically in the context of a nonlinear Archimedes Wave Swing (AWS) device, which functions as a wave energy converter system (WECS) connected to the power grid. The main goal is dynamic performance enhancement of such wave energy systems under network disturbances. Two STA controllers areessential for the rectifier control system in the grid-connected system to capture the most power from waves while limiting losses in the AWS linear generator. In addition, four STA controllers are incorporated into the inverter control loop to preserve the DC link and common coupling voltages at the preset values. The Honey-Badger Algorithm (HBA) is applied for the optimization of gain parameters of the STA controllers, which are compared to the PI gains adjusted using the coot, the hybrid augmented grey wolf optimizer and cuckoo, and PSO search techniques to assess the worthiness of adopting this new control approach. The grid-connected AWS experiences a variety of faults, includingsymmetrical and unsymmetrical faults with successful and unsuccessful breaker reclosures. Finally, the system is experimentally validated using the OP4510 real-time simulator. The experimental results reveal that the HBA-STA controllers outperformPI controllers in omitting fluctuations in the regulated variables, making the STA a strong contender as a control method.

Optimal Design of Fractional-Order PID Controllers for a Nonlinear AWS Wave Energy Converter Using Hybrid Jellyfish Search and Particle Swarm Optimization

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability

Nonlinear impedance matching control for a submerged wave energy converter

AbstractThe impedance matching control, also known as approximate complex conjugate control (ACC), is one of the main strategies for improving the capture of energy by point absorber wave energy converters. Such a strategy shapes the mechanical impedance related to the floater dynamics via the control law. Since the traditional ACC is given by a linear control law, this work proposes a generalization denoted as nonlinear complex conjugate control (NCC) that considers the presence of nonlinear viscous damping in addition to the usual linear damping and stiffness. The energy maximization conditions for the proposed NCC are derived in the frequency domain through the describing function method. These conditions show that the ACC is a special case of the NCC when the total damping on the floater is approximated as a linear function of its velocity. From numerical simulations of a point absorber wave energy converters with nonlinear damping, which is based on the Archimedes wave swing prototype, it is shown that the NCC provides greater energy conversion than the ACC, as well as a robust performance in the presence of variations of the damping coefficient and the excitation force peak frequency.

Nonlinear servocompensator for fault-tolerant control of a wave energy converter

This paper addresses the problem of controlling a wave energy converter (WEC) susceptible to faults in its braking subsystems, characterized through nonlinear damping. By considering the necessity of robust trajectory tracking related to the sea waves for maximizing the converted energy, one aims to preserve such a trajectory in the presence of faults to avoid physical damage in the structure of the WEC. To achieve this objective, this paper proposes a fault-tolerant control (FTC) that combines two systems: (i) a novel nonlinear servocompensator (NSC) and (ii) a fault diagnosis subsystem (FD). The NSC is based on a variable structure control that generalizes the internal model principle for robust tracking. The reference signal is computed from real-time measurements of the irregular sea waves. The FD subsystem estimates the faults related to the wear of the brakes via an unknown input observer. Due to its independent performance from the FD, the global scheme can be considered as a passive FTC. By considering the faulty model of a WEC based on the Archimedes wave swing prototype, theoretical formulation and the convergence proof are given for the NSC and the FD. The performance of the proposed design is verified with numerical simulations of the WEC with the incidence of irregular sea waves under different fault scenarios in the upper and lower brakes.

Triboelectric Nanogenerators Based on the Archimedes Wave Swing for Water Wave Energy Harvesting: Physics Experiments and Simulations

Triboelectric nanogenerators (TENGs) have demonstrated enormous potential as a novel technology for water wave energy harvesting and self‐powered sensors. However, most of the reported studies have focused on sea‐level energy capture. The energy below the sea level including potential energy and kinetic energy is also highly abundant. Herein, a prototype TENG based on the Archimedes wave swing (AWS‐TENG) is proposed. The AWS‐TENG is a unique wave energy conversion device since it is completely submerged and offers excellent concealment. The physical model of AWS‐TENG is developed by investigating structural properties and wave energy theory. Furthermore, a V‐Q‐X equation analysis of the TENG in vertical separation mode is carried out via finite element simulation to better understand its dynamic electrical characteristics. In addition, experimental tests are carried out on the system. The experimental results show that AWS‐TENG achieves a power density output of 7.9 mW m−2 at 4.5 Hz, which is sufficient for fast capacitor charging and application circuits. This proposed TENG design offers a unique way to capture wave energy.

Nearshore Wave Energy Resource Assessment for Off-Grid Islands: A Case Study in Cuyo Island, Palawan, Philippines

Electrifying off-grid and isolated islands in the Philippines remains one of the challenges that hinders community development, and one of the solutions seen to ensure energy security, energy access and promote a low-carbon future is the use of renewable energy sources. This study determines the nearshore wave energy resource during monsoon seasons in Cuyo Island using a 40-year wave hindcast and 8-year on-site wind speed data as inputs to develop a high-resolution wave energy model using SWAN and assesses its annual energy production through matching with wave energy devices. The results show that the average significant wave height (Hs), peak period (Tp) and wave power density (Pd) during a northeast monsoon are Hs = 1.35 m, Tp = 4.79 s and Pd = 4.05 kW/m, respectively, while a southwest monsoon, which is sheltered by the mainland, results in Hs = 0.52 m, Tp = 3.37 s and Pd = 0.34 kW/m. While the simulated model was observed to overestimate the significant wave height (bias = 0.398, RMSE = 0.54 and SI = 1.34), it has a strong relationship with the “observed values” (average r = 0.9). The annual energy production for Wave Dragon, Archimedes Wave Swing and Seawave Slot-Cone Generator are highest at 1970.6 MWh, 2462.04 MWh, 62.424 MWh and 4099.23 MWh, respectively.

Modeling and optimal operation of hybrid wave energy and PV system feeding supercharging stations based on golden jackal optimal control strategy

This paper provides a novel application for the Archimedes wave swing (AWS) device that converts the sea waves into electrical energy using a linear permanent magnet synchronous generator (LPMSG), which is connected to a rectifier that extracts the most significant energy from sea waves and reduces the stator losses. The generator's power combined with a photovoltaic (PV) system provides a total of 250 kW, sufficient for feeding a V3 Tesla Supercharging system, which is currently the latest and most advanced electric vehicle (EV) charging technology. The hybrid system is connected to a DC link, its voltage is stabilized using a supercapacitor energy storage system, and the output is provided to a buck converter that reduces the voltage for the Tesla Supercharger. The control system comprises seven proportional-integral (PI) controllers with four anti-wind back-calculation coefficients to improve transient stability. The PI gains are optimized using the Golden Jackal Optimization Algorithm (GJOA), and the system stability is evaluated by applying different disturbances like a sudden variation in the DC load, temporary DC short circuit, and connection of the EV in various modes such as a grid to vehicle and vehicle to grid modes. The results obtained by the GJOA are compared with the Particle Swarm Optimization and the hybrid Augmented Grey Wolf Optimizer and Cuckoo Search algorithms. Finally, hybrid renewable energy systems can efficiently be employed in EV supercharging stations as validated with the strategy proposed in this work.

Nonlinear Modeling and Real-Time Simulation of a Grid-Connected AWS Wave Energy Conversion System

In this paper, we use a nonlinear model of a grid-connected Archimedes wave swing (AWS) to explore the actual generation potential of this wave energy conversion system. Our model has never been used before for a grid-connected AWS. The control system employs six proportional-integral (PI) controllers to maximize the energy harvest from waves, to minimize the generator power losses, and to maintain both the grid and DC link voltages at their reference values. The PI controller parameters were selected using a hybrid augmented grey wolf optimizer and cuckoo search (AGWO-CS) algorithm. The results obtained from MATLAB Simulink for different types of grid fault, with successful and unsuccessful reclosing of the circuit breakers, were compared with results from the particle swarm optimization (PSO) and COOT algorithms. To verify the accuracy of our control system, a grid-connected AWS was tested experimentally using a real-time RT-LAB simulator combined with OP4510. The results validated the efficiency and superiority of the control system using AGWO-CS and the results from simulation and experiment were similar.

Transient stability improvement of wave energy conversion systems connected to power grid using anti-windup-coot optimization strategy

This paper introduces an enhancement to the transient stability of a wave energy conversion system (WECS) by using the coot optimization algorithm (COA) combined with an anti-windup method. This combination helps in elimination of the windup issue in the integral term of the proportional-integral (PI) controller during power system faults leading to a significant enhancement for the transient stability of the WECS connected to the grid. The COA is utilized to select the PI controller parameters and the anti-windup method back-calculation coefficients. The WECS converts the linear vertical motion into electrical energy by using an Archimedes wave swing device connected to a linear synchronous generator. Minimization of the generator's stator losses and maximization of the generator's real power is accomplished by the utilization of a generator-side converter (GSC). Also, both the point of common coupling voltage and the capacitor link voltage are set at their reference values by utilizing a grid-side inverter (GSI). An optimal design for the PI controllers in both converters is achieved by direct application of the COA to the MATLAB/Simulink model. A comparison is made among the results obtained by the COA and those obtained by other recent optimization algorithms under different grid fault conditions.

Comparison of Estimates of the Excitation Force for Fault Diagnosis in a Wave Energy Converter

This work analyzes the effect of estimating the excitation force in a model-based fault diagnosis (FD) of a wave energy converter (WEC). This force is a non-measurable variable related to the elevation of the sea waves at the location of the floater through a non-causal kernel function. Hence, two strategies are studied here: real-time estimates from predicted wave elevation and smoothed estimates from available measurements. To compare the performance of FD schemes based on each estimate, one considers faults in the damping subsystems of a WEC based on the Archimedes wave swing prototype. The FD scheme is also composed of an unknown input observer for fault detection and an estimator of the magnitude of the faults. Numerical simulations with irregular sea waves show improved diagnosis with smoothed estimates of the excitation force, at the expense of introducing some time delay

A Combined Method of Improved Grey BP Neural Network and MEEMD-ARIMA for Day-Ahead Wave Energy Forecast

Since wave fluctuates continuously, the forecast of the wave energy is very important for the operation of power systems integrated with large-scale wave energy generation. A combined model of day-ahead wave energy forecast based on improved grey BP neural network (BPNN) and modified ensemble empirical mode decomposition (MEEMD) -autoregressive integrated moving average (ARIMA) is proposed in this paper. Firstly, the wave is decomposed into wind waves and swells by wave theories. Secondly, the correlation between wind wave and wind speed is analyzed with improved grey BPNN, and the average height of wind waves can be forecasted based on the historical wind speed data. Thirdly, the MEEMD-ARIMA model is utilized to forecast the average wave height of swells. Thus, combining the wind wave and the swell, the average wave height of the integrated wave can be obtained. Finally, a conversion model from wave elements to wave energy for Archimedes wave swing (AWS) is introduced. A case study using the measured wind and wave data from a real ocean is illustrated, and the effectiveness of the proposed wave energy forecast model is validated.

Improved direct model predictive control for variable magnitude variable frequency wave energy converter connected to constant power load

Archimedes wave swing (AWS) is the first direct-drive wave energy converter (WEC). The AWS is coupled to a linear permanent magnet synchronous generator (LPMSG) and generates a variable magnitude, variable frequency voltage. This paper presents an improved version of direct model predictive control (DMPC) which called improved DMPC (I-DMPC) for a hybrid system containing WEC, supercapacitor (SC) energy storage system (ESS), and constant power load (CPL). This method is implemented based on discrete incremental model of generator and modified objective function, therefor steady-state error is reduced. Simulation results in the MATLAB/SIMULINK environment show the proposed control can maintain stability of the system under different CPL demand and systems faults and improve robustness and fault-tolerant capability of the system in regular and irregular waves. The Comparing results with the conventional DMPC indicate, that by using I-DMPC, d-axis and q-axis errors under regular wave can be reduced 37.5% and 50%, respectively. Also, under irregular wave, I-DMPC reduce the peak of q-axis errors from 73A to 1.5 A when loss of one leg occurred.

Optimal Sliding Mode Control of Permanent Magnet Direct Drive Linear Generator for Grid-Connected Wave Energy Conversion

the key goal of this article is on the design and optimum sliding mode control for Grid-Connected direct drive extraction method of ocean wave energy by Multi-Objective Particle Swarm Optimization (MOPSO). A Linear Permanent Magnet Generator simulates the ocean wave energy extraction system, driven by an Archimedes Wave Swing. Uncontrolled three-phase rectifiers, a three-level buck-boost converter and 3 level neutral point clamped inverter are planned grid integration of Wave Energy Conversion device. The technique monitors the three-level buck-boost converter service cycle linked to the PMLG output terminals and decides the optimum switching sequence of 3 level neutral point clamped inverter to enable the grid relation. Simulations using Matlab/Simulink were carried out to test working of the wave energy converter after the suggested optimal control method was applied under various operating settings. Various simulation test results indicate that the proposed optimum control system is tested in both normal and irregular ocean waves. And it has been shown that the control method of the MOPSO sliding mode is ideal for maximizing energy transfer efficiency. Better voltage management at the DC-link and for achieving greater controllability spectrum was accomplished by the proposed Duty-ratio optimal control system.

Optimal Sliding Mode Control of Permanent Magnet Direct Drive Linear Generator for Grid-Connected Wave Energy Conversion

the key goal of this article is on the design and optimum sliding mode control for Grid-Connected direct drive extraction method of ocean wave energy by Multi-Objective Particle Swarm Optimization (MOPSO). A Linear Permanent Magnet Generator simulates the ocean wave energy extraction system, driven by an Archimedes Wave Swing. Uncontrolled three-phase rectifiers, a three-level buck-boost converter and 3 level neutral point clamped inverter are planned grid integration of Wave Energy Conversion device. The technique monitors the three-level buck-boost converter service cycle linked to the PMLG output terminals and decides the optimum switching sequence of 3 level neutral point clamped inverter to enable the grid relation. Simulations using Matlab/Simulink were carried out to test working of the wave energy converter after the suggested optimal control method was applied under various operating settings. Various simulation test results indicate that the proposed optimum control system is tested in both normal and irregular ocean waves. And it has been shown that the control method of the MOPSO sliding mode is ideal for maximizing energy transfer efficiency. Better voltage management at the DC-link and for achieving greater controllability spectrum was accomplished by the proposed Duty-ratio optimal control system.

Dynamic Stability Improvement of AWS-Based Wave Energy Systems Using a Multiobjective Salp Swarm Algorithm-Based Optimal Control Scheme

This article presents a novel attempt to fine-tune all proportional-integral (PI) controllers of all power electronic circuits based on the salp swarm algorithm (SSA) to enhance both dynamic and transient stability of grid-tied wave energy conversion systems (WECSs). The WECS is based on the Archimedes wave swing unit that is connected with a permanent magnet synchronous generator. It is connected to the grid through a generator side converter (GSC) and a grid-side inverter (GSI). The GSC is controlled to minimize the generator power loss and maximize its power output. The GSI is controlled to maintain the terminal voltage and dc-link voltage via a cascaded control approach. Optimal PI controllers by using the SSA are implemented in these converters. The integral square error criterion is selected as a multiobjective function. The validity of SSA-based-PI controllers is verified by comparing their results with that achieved by genetic algorithm-based-PI controllers at severe disturbance of the power grid. To achieve a realistic study, measured wave pressure data extracted from a Portugal wave power plant are implemented in the dynamic analyses. The effectiveness of SSA-based PI controllers is tested under uncertainty of the real wave pressure of such WECSs. The dynamic studies are extensively performed using PSCAD/EMTDC program.

Ocean wave energy potential along the west coast of the Sumatra island, Indonesia

The past few decades have seen considerable interest in exploration and research of ocean wave energy as a potential energy substitute for fossil-based fuel. In this study, a Wavewatch III spectrum wave model was driven to simulate significant wave height spanning for a period of 25 years, from 1991 to 2015 on the west coast of the island of Sumatra. The 25-year-average of wave energy shows some noticeable hot spots in certain areas that have a value of significant wave height up to 2.33 m and a wave energy 67.29 kW/m. These hotspot occurrences have a similar pattern as statistics collected for the seasonal characteristics that are associated with tropical monsoons with the average value of wave energy reaching its peak in an easterly monsoon season up to 98.21 kW/m, and the lowest average value occurring in the westerly monsoon season, lasting from December to February, with a prevalent value of 10 kW/m. Additional statistical parameters of possible wave energy site selections were considered, such as Coefficient of Variance, Monthly Variability Index, Optimum Hotspot Identifier, Wave Development Index, and accessibility to find the ideal location for wave energy converter deployment. These statistics give insight into potential prospective points for ocean-wave energy harvesting. Eight hotspots were finally selected based on the afore-mentioned statistical considerations and were further analyzed through wave energy characterization and obtained energy calculation through Pelamis, Archimedes Wave Swing, and Wave Dragon Wave Energy Converter power matrices. Finally, inter-annual variability and particular extreme events are discussed.

Transient Stability Augmentation of a Wave Energy Conversion System Using a Water Cycle Algorithm-Based Multiobjective Optimal Control Strategy

This paper introduces a novel optimum design of the proportional–integral (PI) controller in the power converter circuits using the water cycle algorithm (WCA) to augment the transient stability of a grid-connected wave energy conversion (WEC) system. The proposed system relies on the Archimedes wave swing device, which is coupled with a linear permanent magnet synchronous generator (LPMSG). The WEC system is interfaced with the power grid via a generator-side converter (GSC) and a grid-side inverter (GSI). The GSC is used to control both of the d -axis and q -axis current of the LPMSG to minimize its power losses and extract its maximum real power, respectively. The GSI is implemented to control the terminal voltage at the point of common coupling and the dc-link voltage through a complete vector control scheme. The proposed optimal WCA-based PI control strategy is applied to both converters. The optimization process depends on the simulation-based optimization approach. In the proposed approach, the criterion of integral squared error is chosen as a multiobjective function. To validate the proposed WEC system model, the simulation results are compared with the practical results, and their error reaches less than 1%. The effectiveness of the proposed WCA-based PI control strategy is tested and compared with that obtained using the genetic-algorithm-based PI control scheme under symmetrical and unsymmetrical grid fault conditions taking into account a successful and unsuccessful reclosure of circuit breakers. The validity of the proposed control strategy is extensively checked based on simulation studies in the PSCAD/EMTDC environment.

Nearshore wave energy converters comparison and Mediterranean small island grid integration

The aim of the paper is to preliminary analyse the performance of several nearshore Wave Energy Converters (WEC) sited in the west coast of Sicily close to the Favignana island’s shorelines. In particular, the four WEC technologies that have been considered are Wave Star, Oyster, Wave Dragon and Archimedes Wave Swing. The power performance assessment has been developed through the comparison of various scaled versions of the WEC using a proved scaling process based on the Froude similitude. The best device for Favignana’s wave climate has resulted to be the Wave Dragon with a rated power of 500 kW. Then, the evaluation of the Wave Dragon’s effect on the off-grid system of Favignana has been analysed by means of the HOMER software. The current mix of production and the electric load of the island have been modelled on HOMER and the introduction of the Wave Dragon with and without a battery energy storage has been analysed. The obtained results underlined that high fuel savings and emission reductions can be achieved. The study has been carried out in the context of the PRISMI (Promoting RES Integration for Smart Mediterranean Islands) international project funded by the Interreg-MED EU programme.