Education and Outreach
 

Turbine Operation

Deep Learning for Marine Renewable Energy Cybersecurity

Led by Yufei Tang, Ph.D.
Affiliated Home Campus: Boca Raton
Affiliated Department: Computer and Electrical Engineering and Computer Science
In-Person or Remote project

PROJECT

The operation & maintenance (O&M) cost of marine renewable energy devices will be high because of access limitations to performing O&M due to the geographical location (deep sea, river, remote areas), requiring both technical and physical skill (engineer-diver), and harsh environment (underwater, salty corrosive liquid medium). This REU project will focus on reducing O&M costs and increasing ocean current turbine (OCT) resilience by remotely monitoring turbine health using multi-modal system measurements and designing fault tolerant strategies. Specifically, the research plan for the Summer 2020 is as follows: a. Fault self-recovery design for fault tolerant operation using artificial intelligence (AI); b. Collecting two types of data for extensive testing and validation. One will be collected from a high-fidelity MHK simulation platforms using the National Renewable Energy Laboratory’s Fatigue, Aerodynamics, Structures, and Turbulence (FAST) code and an in-house developed moored OCT numerical simulation. The other will be collected from a dynamometer bench with a 3-kW emulated OCT.

The project will provide a meaningful experience for the participant, while contributing to Dr. Tang’s ongoing work in this area (see Dr. Tang’s research website http://faculty.eng.fau.edu/tangy/). Selected participants will develop engineering skill sets related to marine renewable energy generation, electrical power systems, and cybersecurity. Each of these skill sets will be exercised when developing numerical techniques, while the student is simultaneously introduced to the emerging marine renewable energy field. The student will be introduced to machine learning and data mining as this project will be specifically tailored to evaluate advanced adversarial events detection algorithms. Soft skills will be advanced during this internship through the preparation of a final report summarizing their research and findings, through the development and delivery of one or more presentations, and likely through the development of a conference/journal publication. This work is the continuation of previous REU site efforts by multiple Undergraduate Researchers under the supervision of Yufei Tang (see 2018-2020 “Projects and Participants” on this website).

HBOI ocean fish