Search for: All records

The complexity and variability of ocean waves make wave energy harvesting very challenging. Previous research has indicated that wave energy was mainly generated and transferred by wind, but the detailed correlation between wind and wave energy has not been discovered. Wave energy in the Gulf of Mexico (GoM) has high variability with distinct seasonal behavior. However, the underlying reasons for this unique behavior have not been discussed and discovered yet. In this paper, a computer animation-based dynamic visualization method was created to conduct exploratory and explanatory analyses of 36 years of meteorological data in the GoM from the WaveWatch III system to identify preliminary patterns and underlying reasons for the unique behavior of wave energy in the GoM. These preliminary patterns and underlying reasons were further analyzed using Energy Events and Breaks concepts. During both high and low levels wave energy periods, the detailed correlation between wave energy and the wind was analyzed and determined. High level wave power in the GoM was mainly generated by the local inland wind from northern weather patterns, while low level wave power was mainly generated by swells from the Caribbean and the Atlantic oceans, which entered the GoM through the two narrow pathways, the Straits of Yucatan and the Florida Straits. The results from this paper will also be able to help the design, placement, and operation of future wave energy converters to improve their efficiency in harvesting wave energy in the GoM. 

Wave energy has been studied and explored because of its enormous potential to supply electricity for human activities. However, the uncertainty of its spatial and temporal variations increases the difficulty of harvesting wave energy commercially. There are no large-scale wave converters in commercial operation yet. A thorough understanding of wave energy dynamic behaviors will definitely contribute to the acceleration of wave energy harvesting. In this paper, about 40 years of meteorological data from the Gulf of Mexico were obtained, visualized, and analyzed to reveal the wave power density hotspot distribution pattern, and its correlation with ocean surface water temperatures and salinities. The collected geospatial data were first visualized in MATLAB. The visualized data were analyzed using the deep learning method to identify the wave power density hotspots in the Gulf of Mexico. By adjusting the temporal and spatial resolutions of the different datasets, the correlations between the number of hotspots and their strength levels and the surface temperatures and salinities are revealed. The R value of the correlation between the wave power density hotspots and the salinity changes from −0.371 to −0.885 in a negative direction, and from 0.219 to 0.771 in a positive direction. For the sea surface temperatures, the R values range from −0.474 to 0.393. Certain areas within the Gulf of Mexico show relatively strong correlations, which may be useful for predicting the wave energy behavior and change patterns. 

Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification. 

For a heaving point absorber to perform optimally, it has to be designed to resonate to the prevailing ocean wave period. Hence, it is important to make the ocean wave data analysis to be as accurate as possible. In this study, existing wave condition data is used to investigate the effect of the temporal resolution (daily vs. hourly) of wave data on the design of the device and power capture. The temporal resolution effect on the estimation of ocean wave resource theoretical potential is also investigated. Results show that the temporal resolution variation of the ocean wave data affects the design of the device and its power capture, but the theoretical power resource assessment is not significantly affected. The device designed for the Gulf of Mexico is also analyzed with wave condition in Oregon, which has about 40 times the wave resource theoretical potential compared to the Gulf of Mexico. The results confirmed that a device should be designed for a specific location as the device performed better in the Gulf of Mexico, which has much less ocean wave resource theoretical potential. At last, the effect of the design, diameter and season (summer and winter) on the power output of the device is also investigated using statistical hypothesis testing methods. The results show that the power capture of a device is significantly affected by these parameters. 

Different concepts and methods have been proposed and developed by many researchers to harvest ocean wave energy. In this paper, a new self-adjustable wave energy converter concept is presented, which changes its inertia through ballasting and de-ballasting using sea water. The trigger of ballasting and de-ballasting is controlled by the critical wave period. Therefore, the self-adjustable wave energy converter is able to interact at resonance with the ocean waves at two different resonant bandwidths. Ten years real wave data with hourly resolution from a selected location in Gulf of Mexico was used in this paper to decide the critical wave period and other parameters of the wave energy converter. The annual energy performance of the self-adjustable wave energy converter was also estimated and compared with non-adjustable wave energy converter with similar dimensions. Structural analysis including both static and fatigue analysis was performed on the self-adjustable wave energy converter to determine its survivability with the real ocean wave data. The results show that the self-adjustable wave energy converter is able to capture more energy than non-adjustable wave energy converter, and is able to survive during the hash ocean wave conditions.