scholarly journals HF Radars for Wave Energy Resource Assessment Offshore NW Spain

2021 ◽  
Vol 13 (11) ◽  
pp. 2070
Author(s):  
Ana Basañez ◽  
Vicente Pérez-Muñuzuri
Keyword(s):  
Wave Energy ◽  
Numerical Models ◽  
Wave Spectrum ◽  
Energy Resource ◽  
Electrical Energy ◽  
Resource Assessment ◽  
Electricity Production ◽  
Statistical Validation ◽  
Wave Energy Converters ◽  
Energy Converters

Wave energy resource assessment is crucial for the development of the marine renewable industry. High-frequency radars (HF radars) have been demonstrated to be a useful wave measuring tool. Therefore, in this work, we evaluated the accuracy of two CODAR Seasonde HF radars for describing the wave energy resource of two offshore areas in the west Galician coast, Spain (Vilán and Silleiro capes). The resulting wave characterization was used to estimate the electricity production of two wave energy converters. Results were validated against wave data from two buoys and two numerical models (SIMAR, (Marine Simulation) and WaveWatch III). The statistical validation revealed that the radar of Silleiro cape significantly overestimates the wave power, mainly due to a large overestimation of the wave energy period. The effect of the radars’ data loss during low wave energy periods on the mean wave energy is partially compensated with the overestimation of wave height and energy period. The theoretical electrical energy production of the wave energy converters was also affected by these differences. Energy period estimation was found to be highly conditioned to the unimodal interpretation of the wave spectrum, and it is expected that new releases of the radar software will be able to characterize different sea states independently.

scholarly journals Weptos Wave Energy Converters to Cover the Energy Needs of a Small Island

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 423 ◽  
Author(s):  
Lucia Margheritini ◽  
Jens Kofoed
Keyword(s):  
Wave Energy ◽  
Energy Resource ◽  
Energy System ◽  
Resource Assessment ◽  
Small Island ◽  
Wave Energy Converters ◽  
Energy Needs ◽  
Battery Bank ◽  
The Individual ◽  
Energy Converters

This paper presents the details of a study performed to investigate the feasibility of a wave energy system made up of a number of Weptos wave energy converters (WECs) and sets of batteries, to provide the full energy demands of a small island in Denmark. Two different configurations with 2 and 4 Weptos machines respectively with a combined installed power of 750 kW (and a capacity factor of 0.2) are presented. One full year simulation, based a detailed hourly analysis of the power consumption and wave energy resource assessment in the surrounding sea, is used to demonstrate that both configurations, supplemented by a 3 MWh battery bank and a backup generator, can provide the energy needs of the island. The proposed configurations are selected on the basis of a forecast optimization of price estimates for the individual elements of the solutions. The simulations show that Weptos WECs actually deliver 50% more than average consumption over the year, but due to the imbalance between consumption and production, this is not enough to cover all situations, which necessitates a backup generator that must cover 5–7% of consumption, in situations where there are too few waves and the battery bank is empty.

Numerical Modeling to Aid in the Structural Health Monitoring of Wave Energy Converters

2013 ◽  
Vol 569-570 ◽  
pp. 595-602 ◽  
Author(s):  
William Finnegan ◽  
Jamie Goggins
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Wave Energy ◽  
Numerical Models ◽  
Wave Structure ◽  
Ocean Waves ◽  
Test Site ◽  
Structural Health ◽  
Wave Energy Converters ◽  
Energy Converters

A vital aspect of ensuring the cost effectiveness of wave energy converters (WECs) is being able to monitor their performance remotely through structural health monitoring, as these devices are deployed in very harsh environments in terms of both accessibility and potential damage to the devices. The WECs are monitored through the use of measuring equipment, which is strategically placed on the device. This measured data is then compared to the output from a numerical model of the WEC under the same ocean wave conditions. Any deviations would suggest that there are problems or issues with the WEC. The development of accurate and effective numerical models is necessary to minimise the number of times the visual, or physical, inspection of a deployed WEC is required. In this paper, a numerical wave tank model is, first, validated by comparing the waves generated to those generated experimentally using the wave flume located at the National University of Ireland, Galway. This model is then extended so it is suitable for generating real ocean waves. A wave record observed at the Atlantic marine energy test site has been replicated in the model to a high level of accuracy. A rectangular floating prism is then introduced into the model in order to explore wave-structure interaction. The dynamic response of the structure is compared to a simple analytical solution and found to be in good agreement.

scholarly journals A Deep Learning and GIS Approach for the Optimal Positioning of Wave Energy Converters

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6773
Author(s):  
Georgios Batsis ◽  
Panagiotis Partsinevelos ◽  
Georgios Stavrakakis
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Time Series ◽  
Wave Energy ◽  
Renewable Energy Sources ◽  
Heterogeneous Data ◽  
Electricity Production ◽  
Final Decision ◽  
Wave Energy Converters ◽  
Energy Converters

Renewable Energy Sources provide a viable solution to the problem of ever-increasing climate change. For this reason, several countries focus on electricity production using alternative sources. In this paper, the optimal positioning of the installation of wave energy converters is examined taking into account geospatial and technical limitations. Geospatial constraints depend on Land Use classes and seagrass of the coastal areas, while technical limitations include meteorological conditions and the morphology of the seabed. Suitable installation areas are selected after the exclusion of points that do not meet the aforementioned restrictions. We implemented a Deep Neural Network that operates based on heterogeneous data fusion, in this case satellite images and time series of meteorological data. This fact implies the definition of a two-branches architecture. The branch that is trained with image data provides for the localization of dynamic geospatial classes in the potential installation area, whereas the second one is responsible for the classification of the region according to the potential wave energy using wave height and period time series. In making the final decision on the suitability of the potential area, a large number of static land use data play an important role. These data are combined with neural network predictions for the optimizing positioning of the Wave Energy Converters. For the sake of completeness and flexibility, a Multi-Task Neural Network is developed. This model, in addition to predicting the suitability of an area depending on seagrass patterns and wave energy, also predicts land use classes through Multi-Label classification process. The proposed methodology is applied in the marine area of the city of Sines, Portugal. The first neural network achieves 98.7% Binary Classification accuracy, while the Multi-Task Neural Network 97.5% in the same metric and 93.5% in the F1 score of the Multi-Label classification output.

On the Importance of High–Fidelity Numerical Modelling of Ocean Wave Energy Converters under Controlled Conditions

2022 ◽  
Author(s):  
C. Windt
Keyword(s):  
Numerical Modelling ◽  
Wave Energy ◽  
Numerical Models ◽  
Ocean Wave ◽  
High Fidelity ◽  
Wave Energy Converters ◽  
Ocean Wave Energy ◽  
Controlled Conditions ◽  
Hydrodynamic Wave ◽  
Energy Converters

Abstract. Numerical modelling tools are commonly applied during the development and optimisation of ocean wave energy converters (WECs). Models are available for the hydrodynamic wave structure interaction, as well as the WEC sub–systems, such as the power take–off (PTO) model. Based on the implemented equations, different levels of fidelity are available for the numerical models. Specifically under controlled conditions, with enhance WEC motion, it is assumed that non-linearities are more prominent, re- quiring the use of high–fidelity modelling tools. Based on two different test cases for two different WECs, this paper highlights the importance of high–fidelity numerical modelling of WECs under controlled conditions.

Estimation of Technical Wave Energy Potential in Exclusive Economic Zone of India

2018 ◽  
Author(s):  
Garlapati Nagababu ◽  
Ravi Patel ◽  
Seemanth Moideenkunju ◽  
Abhinaya Srinivas Bhasuru ◽  
Surendra Singh Kachhwaha ◽  
...  
Keyword(s):  
Wave Energy ◽  
Coastal Region ◽  
Resource Assessment ◽  
Capacity Factor ◽  
Eastern Coast ◽  
Wave Power ◽  
Western Coast ◽  
Energy Potential ◽  
Wave Energy Converters ◽  
Energy Converters

Identification of the best location for wave farm installation, wave resource assessment needs to be carried out. In the present work, wave resource assessment along the Indian EEZ was carried out using the 17-year (2000 to 2016) output simulation of the third generation wave model WAVEWATCH-III (WWIII). Spatial distribution of significant wave height, mean wave energy period and annual mean of wave power is plotted. Further, the monthly and seasonal variation has been carried out to assess the effect on temporal variability at a specific location. The results show the annual mean wave power is in the range of 1–12 kW/m across the Indian EEZ. Further, it was observed that wave power along the western coast of India is more energetic than the eastern coast of India, with annual average wave power of 8–12 kW/m and 2–6 kW/m respectively. However, coastlines of Gujarat and Maharashtra experience the maximum seasonal and monthly variability across Indian EEZ, which is 2 and 3.5 respectively. By using different wave energy converters (WEC), the capacity factor and technical wave energy potential over the study area are estimated. Oceantec WEC shows maximum capacity factor (0.35) among the all selected wave energy converters. The results reveal that the electric wave power generation is 3 times more in the western coastal region as compared to the eastern coast of India.

scholarly journals A Design Outline for Floating Point Absorber Wave Energy Converters

2014 ◽  
Vol 6 ◽  
pp. 846097 ◽  
Author(s):  
Mohammed Faizal ◽  
M. Rafiuddin Ahmed ◽  
Young-Ho Lee
Keyword(s):  
Wave Energy ◽  
Power Output ◽  
Energy Resource ◽  
Wave Structure ◽  
Floating Point ◽  
Mooring Lines ◽  
Point Absorber ◽  
Wave Energy Converters ◽  
Incident Waves ◽  
Energy Converters

An overview of the most important development stages of floating point absorber wave energy converters is presented. At a given location, the wave energy resource has to be first assessed for varying seasons. The mechanisms used to convert wave energy to usable energy vary for different wave energy conversion systems. The power output of the generator will have variations due to varying incident waves. The wave structure-interaction leads to modifications in the incident waves; thus, the power output is also affected. The device has to be stable enough to prevent itself from capsizing. The point absorber will give optimum performance when the incident wave frequencies correspond to the natural frequency of the device. The methods for calculating natural frequencies for pitching and heaving systems are presented. Mooring systems maintain the point absorber at the desired location. Various mooring configurations as well as the most commonly used materials for mooring lines are discussed. An overview of scaled modelling is also presented.

scholarly journals Wave energy resource variation off the west coast of Ireland and its impact on realistic wave energy converters’ power absorption

2018 ◽  
Vol 224 ◽  
pp. 205-219 ◽  
Author(s):  
Markel Penalba ◽  
Alain Ulazia ◽  
Gabriel Ibarra-Berastegui ◽  
John Ringwood ◽  
Jon Sáenz
Keyword(s):  
Wave Energy ◽  
West Coast ◽  
Energy Resource ◽  
The West ◽  
Power Absorption ◽  
Wave Energy Converters ◽  
Energy Converters ◽  
Resource Variation

scholarly journals Wave Energy Assessment and Wind Correlation for the North Region of Scotland, Hindcast Resource and Calibration, Investigating for Improvements of Physical Model for Adaptation to Temporal Correlation

2014 ◽  
Author(s):  
George Lavidas ◽  
Vengatesan Venugopal ◽  
Daniel Friedrich ◽  
Atul Argawal
Keyword(s):  
Wave Energy ◽  
Numerical Models ◽  
Energy Resource ◽  
Temporal Correlation ◽  
Wave Model ◽  
Resource Assessment ◽  
Wave Modelling ◽  
Energy Assessment ◽  
The North ◽  
Set Up

Wave energy sites around Scotland, are considered one of the most energetic waters, as they are exposed to the Atlantic Ocean. The amount of energy reaching the shoreline provides an opportunity for wave energy deployments. Currently, considerations on wave devices expect them to be installed at nearshore locations. That means that the potential wave resource has to be investigated, since deep to shallow water interactions alter the shape of propagated waves. Resource assessment for these regions is essential in order to estimate the available and extractable energy resource. Although several numerical models exist for wave modelling, not all are suitable for nearshore applications. For the present work, the nearshore wave model SWAN has been used to simulate waves for the Hebridean region. The set-up, calibration and validation of the model are discussed. The resulting wave conditions are compared with buoy measurements. Results indicate that the modelling technique performed well.

scholarly journals Ocean Energy Systems Wave Energy Modelling Task: Modelling, Verification and Validation of Wave Energy Converters

2019 ◽  
Vol 7 (11) ◽  
pp. 379 ◽  
Author(s):  
Wendt ◽  
Nielsen ◽  
Yu ◽  
Bingham ◽  
Eskilsson ◽  
...  
Keyword(s):  
Wave Energy ◽  
Potential Flow ◽  
Numerical Models ◽  
Energy Systems ◽  
Verification And Validation ◽  
Ocean Energy ◽  
Fully Nonlinear ◽  
Wave Energy Converters ◽  
Modelling Task ◽  
Energy Converters

The International Energy Agency Technology Collaboration Programme for Ocean Energy Systems (OES) initiated the OES Wave Energy Conversion Modelling Task, which focused on the verification and validation of numerical models for simulating wave energy converters (WECs). The long-term goal is to assess the accuracy of and establish confidence in the use of numerical models used in design as well as power performance assessment of WECs. To establish this confidence, the authors used different existing computational modelling tools to simulate given tasks to identify uncertainties related to simulation methodologies: (i) linear potential flow methods; (ii) weakly nonlinear Froude–Krylov methods; and (iii) fully nonlinear methods (fully nonlinear potential flow and Navier–Stokes models). This article summarizes the code-to-code task and code-to-experiment task that have been performed so far in this project, with a focus on investigating the impact of different levels of nonlinearities in the numerical models. Two different WECs were studied and simulated. The first was a heaving semi-submerged sphere, where free-decay tests and both regular and irregular wave cases were investigated in a code-to-code comparison. The second case was a heaving float corresponding to a physical model tested in a wave tank. We considered radiation, diffraction, and regular wave cases and compared quantities, such as the WEC motion, power output and hydrodynamic loading.

A Machine Learning Approach to the Analysis of Wave Energy Converters

2015 ◽  
Author(s):  
Dripta Sarkar ◽  
Emile Contal ◽  
Nicolas Vayatis ◽  
Frederic Dias
Keyword(s):  
Wave Energy ◽  
Numerical Models ◽  
Computational Time ◽  
Wave Energy Converters ◽  
Machine Learning Approach ◽  
Input Variables ◽  
Training Examples ◽  
The Individual ◽  
Energy Conversion Devices ◽  
Energy Converters

The hydrodynamic analysis and estimation of the performance of wave energy converters (WECs) is generally performed using semi-analytical/numerical models. Commercial boundary element codes are widely used in analyzing the interactions in arrays comprising of wave energy conversion devices. However, the analysis of an array of such converters becomes computationally expensive, and the computational time increases as the number of devices in the system is increased. As such determination of optimal layouts of WECs in arrays becomes extremely difficult. In this study, an innovative active experimental approach is presented to predict the behaviour of theWECs in arrays. The input variables are the coordinates of the center of the wave energy converters. Simulations for training examples and validation are performed for an array of OscillatingWave Surge Converters, using the mathematical model of Sarkar et. al. (Proc. R. Soc. A, 2014). As a part of the initial findings, results will be presented on the performance of wave energy converters located well inside an array. The broader scope/aim of this research would be to predict the behaviour of the individual devices and overall performance of the array for arbitrary layouts of the system and then identify optimal layouts subject to various constraints.

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