Received research funding from the Ocean Energy Safety Institute (OESI)

2025-07-01

  • Our project entitled “Real-Time Monitoring and Visualization System for Marine Energy Devices” has been selected by OESI in response to the OESI Request for Proposals (RFP) Cycle II.

  • Out of 91 proposals, only 14 were selected for funding.

  • Its official selection was annouced on the OESI webpage on July 1, 2025.

  • The total project funding from OESI is $321,618. It is a mixed team of academic universities and industry partners. The lead institute is Florida Atlantic University. Our lab at UH, as a subawardee, receives $70,000.

Project Abstract:

  • Marine energy faces significant challenges, such as the high energy density of fluids leading to high torque and the corrosive marine environment (e.g., corrosion, biofouling), which increases life-cycle costs. Operation and maintenance of marine energy devices are expected to account for 26-32% of their levelized cost of electricity (LCOE). This project aims to develop real-time, low-cost prognostic condition monitoring (PCM) for marine energy devices, including wave energy converters and marine current turbines.
  • The project team consists of distinguished experts in marine energy and related fields. Prof. Yufei Tang from Florida Atlantic University (FAU), a leader in PCM for marine energy devices, will coordinate the team and guide the development of innovative monitoring tools. Prof. James VanZwieten, also from FAU, brings extensive field-testing expertise to ensure practical validation of the systems. Dr. Xingpeng Li, an Associate Professor at the University of Houston, specializes in power systems and renewable energy integration, contributing advanced computational techniques. Dr. Jia Mi, an Assistant Professor at Stevens Institute of Technology, adds valuable experience in WEC development. Consultant Jeremiah Mendez, Senior Director at Ocean Power Technologies (OPT), brings over two decades of expertise in real-time monitoring systems for marine energy devices, playing a key role in ensuring operational performance and system integrity.
  • This project will utilize real-time sensor data, applying advanced signal processing and deep learning techniques for cognitive detection and prediction. This will enable online device health monitoring, detect abnormal performance, and assess the rate of degradation, ensuring timely maintenance. Importantly, the PCM system will rely largely on existing control system data, eliminating the need for additional sensors, ensuring a low-cost nonintrusive solution. Additionally, we will develop a novel data visualization dashboard for the PCM system, leveraging OPT’s PowerBuoy control and management system and PowerGPT, a patent-pending product developed by the project lead PI. PowerGPT integrates natural language processing (NLP) with advanced data processing features, enabling users to submit prompts and receive detailed feedback. Its cloud-based engine ensures accessibility, and its user-friendly interface allows even those with limited knowledge to easily access powerful system monitoring functionalities.