Investigating Wave Energy Potential in Southern Coasts of the Caspian Sea Using Grey Wolf Optimizer Algorithm
There is a significantly accelerating trend in the application of the marine wave energy converters in recent years. As a result, it is imperative to adopt a suitable point for implementing these systems. Besides, the Caspian Sea, as one of the most important marine renewable energy sources in Asia, is capable of supplying the coastal areas with a large amount of energy. Therefore, areas around nine ports in the southern coasts of the Caspian Sea were selected to measure their wave energy potential. Initially, the amount of energy on these points was measured using the irregular energy theory. It was observed that the wave power was higher in the southwestern areas (within the Kiashahr coast and Anzali port) than the southeastern areas. A new approach was developed to compare these points and measure their fitnesses in supplying the maximum energy using the Grey Wolf optimizer (GWO) algorithm and time history analysis. In this method, the optimal parameters were first extracted from the algorithm for assessing the points within the southern areas of the Caspian Sea. These values were regarded as the assessment indices. Then, the fitness of each point was obtained using the correlation function and the norm vector to present the most optimal position with maximum wave energy exploitation potential. This new approach was validated with analytical data, and its accuracy in predicting and comparing the wave power on different points was approved. Finally, by a side-by-side comparison of the parameters affecting the wave energy, the optimum range of significant wave height and wave energy period was achieved.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Optimization Study of Hydraulic Power Take-off System for an Ocean Wave Energy Converter
Ocean wave renewable energy is fast becoming a key part of renewable energy industries over the recent decades. By developing wave energy converters as the main converter technology in this process, their power take-off …
Estimator Model for Prediction of Power Output of Wave Farms Using Machine Learning Methods
The amount of power generated by a wave farm depends on the Wave Energy Converter (WEC) arrangement along with the usual wave conditions. Therefore, forming the appropriate arrangement of WECs in an array is an important…
BIG-bench Machine LearningSite-dependent Solutions of Wave Energy Converter Farms with Surrogate Models, Control Co-design, and Layout Optimization
Design of wave energy converter farms entails multiple domains that are coupled, and thus, their concurrent representation and consideration in early-stage design optimization has the potential to offer new insights and …
Crust Macrofracturing as the Evidence of the Last Deglaciation
Machine learning methods were applied to reconsider the results of several passive seismic experiments in Finland. We created datasets from different stages of the receiver function technique and processed them with one …
AttributeBIG-bench Machine LearningManaging Renewable Energy Resources Using Equity-Market Risk Tools - the Efficient Frontiers
The energy market, and specifically the renewable sector carries volatility and risks, similar to the financial market. Here, we leverage on a well-established, return-risk approach, commonly used by equity portfolio-man…
Management