A DATASET AT A GLANCE...

The growing energy demand and the goal to cut down fossil-fuel usage has created a huge dependency on renewable energy resources these days. Marine energy is one of the renewable energy resources, wherein tidal or wave energy from the oceans are acquired to generate power. But it is not that easy to achieve a marine energy conversion system, which is highly-stable to provide efficient power generation in a turbulent-prone, variable and unpredictable oceanic environment!!! Further, even if optimization or modelling of system parameters seem to improve power generation in such an environment, testing such systems in real-time might incur huge costs associated with system installment or result in direct system failure. Hence, the researchers trying to augment marine power generation with reduced costs or system failure might necessitate a dataset to bridge the gap between their simulated model and real-time operation. “Optimization of Marine Energy Conversion Systems Through Modeling, Optimization, and CHIL Validation” is one such publicly-available dataset that serves this purpose. Using this dataset from the Marine and Hydrokinetic Data Repository (https://mhkdr.openei.org/submissions/497), the researchers can model better system configurations or effective control strategies to yield optimal tidal energy conversion. Additionally, as the dataset includes Controller-Hardware-in-the-Loop (CHIL) validation, the researchers could safely test or verify their optimally-designed controllers for any faults and maximize the tidal energy conversion in realistic scenarios. Not only that, this dataset also includes MATLAB codes and Simulink models of Tidal Energy Conversion System (TECS) to aid the researchers contributing towards this notion. However, a citation request has to be made by the researchers using the dataset as stated below.

• Luo, F., Singh, D., & Hassan, M. (2023). Optimization of Marine Energy Conversion Systems Through Modeling, Optimization, and CHIL Validation. [Data set]. Marine and Hydrokinetic Data Repository. Stony Brook University. https://doi.org/10.15473/1997352