Spatial interactions among oscillating wave energy converters: Electricity production and power quality issues

2016 ◽  
Author(s):  
Federica Bizzozero ◽  
Silvia Bozzi ◽  
Giambattista Gruosso ◽  
Giuseppe Passoni ◽  
Marianna Giassi
Keyword(s):  
Power Quality ◽  
Wave Energy ◽  
Electricity Production ◽  
Spatial Interactions ◽  
Wave Energy Converters ◽  
Quality Issues ◽  
Energy Converters

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 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.

scholarly journals Switching sequences for non-predictive declutching control of wave energy converters

2020 ◽  
Vol 53 (2) ◽  
pp. 12295-12300
Author(s):  
Paula B. Garcia-Rosa ◽  
Olav B. Fosso ◽  
Marta Molinas
Keyword(s):  
Wave Energy ◽  
Wave Energy Converters ◽  
Energy Converters

scholarly journals Layout optimization of heaving Wave Energy Converters linear arrays in front of a vertical wall

2021 ◽  
Author(s):  
Eva Loukogeorgaki ◽  
Constantine Michailides ◽  
George Lavidas ◽  
Ioannis K. Chatjigeorgiou
Keyword(s):  
Wave Energy ◽  
Vertical Wall ◽  
Layout Optimization ◽  
Linear Arrays ◽  
Wave Energy Converters ◽  
Energy Converters

scholarly journals Model Predictive Control for Wave Energy Converters: A Moving Window Blocking Approach

2020 ◽  
Vol 53 (2) ◽  
pp. 12815-12821
Author(s):  
Juan Guerrero-Fernández ◽  
Oscar J. González-Villarreal ◽  
John Anthony Rossiter ◽  
Bryn Jones
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Moving Window ◽  
Wave Energy Converters ◽  
Energy Converters

Design of a new turbine for OWC Wave Energy Converters: the DDT concept

2020 ◽  
Author(s):  
Manuel García-Díaz ◽  
Bruno Pereiras ◽  
Celia Miguel-González ◽  
Laudino Rodríguez ◽  
Jesús Fernández-Oro
Keyword(s):  
Wave Energy ◽  
Wave Energy Converters ◽  
Energy Converters

scholarly journals Hardware-in-the-Loop Validation of Model Predictive Control of a Discrete Fluid Power Power Take-Off System for Wave Energy Converters

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3668
Author(s):  
Anders H. Hansen ◽  
Magnus F. Asmussen ◽  
Michael M. Bech
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Fluid Power ◽  
Wave Power ◽  
Reactive Control ◽  
Power Extraction ◽  
Wave Energy Converters ◽  
Extraction Algorithm ◽  
Energy Converters

Model predictive control based wave power extraction algorithms have been developed and found promising for wave energy converters. Although mostly proven by simulation studies, model predictive control based algorithms have shown to outperform classical wave power extraction algorithms such as linear damping and reactive control. Prediction models and objective functions have, however, often been simplified a lot by for example, excluding power take-off system losses. Furthermore, discrete fluid power forces systems has never been validated experimentally in published research. In this paper a model predictive control based wave power extraction algorithm is designed for a discrete fluid power power take-off system. The loss models included in the objective function are based on physical models of the losses associated with discrete force shifts and throttling. The developed wave power extraction algorithm directly includes the quantized force output and the losses models of the discrete fluid power system. The experimental validation of the wave power extraction algorithm developed in the paper shown an increase of 14.6% in yearly harvested energy when compared to a reactive control algorithm.

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