“Stay Ashore” … Offshore Wind O&M Automation - Guiju Song, GE Research
Guiju Song, Platform Leader, Offshore Wind, at GE Research located in Niskayuna, NY
Abstract: Offshore wind energy is critical to realize the world’s greenhouse gas reduction goal. In the US, the Biden administration has set up the goal of deploying 30GW of offshore wind capacity by 2030. Reducing the levelized cost of energy (LCOE) is key to the large-scale deployment. Offshore operation & maintenance (O&M) cost could potentially contribute to 35% of the LCOE. Therefore, GE launched the “Stay Ashore” O&M strategy aiming to apply digital innovations and automation to significantly lower the offshore O&M cost. The “Stay Ashore” vision contains four element steps – connect, analyze, manage, and optimize. This seminar focuses on deep diving GE’s research projects funded by the National Offshore Wind Research and Development Consortium (NOWRDC) as the examples for the two steps of “analyze” and “manage”.
The first project is titled as “Enabling condition based- maintenance for offshore wind”. Applying digital innovations such as AI and digital twin to enable condition-based maintenance (CBM) has been proved to be effective in many industries. However, the offshore wind farms in operation today in the US water only have 42 MW of capacity commissioned in the recent few years. There is not a lot of operational history and data to track. Therefore, one of the key challenges in implementing AI and digital twin for offshore wind is lacking historical operational data required for AI/ML model development and validation. Our team was funded by NOWRDC to investigate and develop technical solutions to realize full CBM in offshore wind. To address the data sparsity challenges, we first explored the feasibility of utilizing physics-based models (digital twins) to augment the limited available operational data. Second, an AI/ML modeling method was carefully selected and developed to accurately estimate blade health state over time. Third, the limited data collected from the real-world turbines was utilized to calibrate the AI model.
The second project is titled as “Autonomous Vessel-Based Multi-Sensing System for Inspection and Monitoring”. The current available inspection and monitoring strategies for offshore wind are either cost prohibitive or limited by varying offshore operational conditions such as weather and sea states. Our project aims to develop a multi-sensing system to conduct robust inspection at varying sea conditions. Such system can enable adaptation of proven technologies from onshore to offshore wind, advance offshore O&M strategy, reduce LCOE, and accelerate the U.S.’s ability to economically deploy offshore wind technology.
At last, I will summarize the key ingredients in our success of completing the first project and elaborate how this innovation can be leveraged and expanded to other applications. I will then reiterate our perspectives on the “Stay Ashore” vision for the offshore wind O&M.
About: Dr. Guiju Song is currently the Platform Leader, Offshore Wind, at GE Research located in Niskayuna, NY. In this role, Dr. Song leads the strategic growth and multi-generation technology planning process for Offshore Wind at GE Research. She manages a multi-million R&D project portfolio and supports transitioning the technologies developed by GE Research to new products of GE’s Offshore business. Dr. Song is also a Principal Investigator (PI) and a T2M (technology to market) manager for GE’s government funded Offshore Wind projects. Prior to her current role, Dr. Song led a team to develop artificial intelligence (AI) and digital technologies to solve challenging industrial problems in the renewable energy and aerospace industries. During her 18+ years of professional experience, Dr. Song has been instrumental in leading multi-disciplinary teams, bridging AI/ML with physics to enable better industrial asset reliability and performance management. As a result of her innovative work in this cross-disciplinary area, Dr. Song has filed 31 US patents. Before joining GE, Dr. Song conducted postdoctoral research in Oregon Health & Science University on Bio-medical engineering. and received her Ph.D. in Optical Engineering from the Chinese Academia of Sciences in 2001.