Dr Zi Lin

Research Associate

Naval Architecture, Ocean and Marine Engineering

Personal statement

Dr Zi Lin is a Research Associate and a member of the Offshore Engineering Institute at the University of Strathclyde since 2018. Before Strathclyde, she was a Research Fellow in the School of Water, Energy, and Environment, Cranfield University. She holds a PhD degree from the University of Strathclyde; an MSc in Civil Engineering and a BEng (Hons) in Mechanical Engineering, both from Dalian University of Technology, China. Her main area of expertise is regarding renewable energy, through advanced numerical simulation and machine learning. To be involved in the energy sector outside of academia, she is a full member of AXELOS. Apart from it, she is a frequent presenter and session-chair (35th, 37th and 38th; Ocean Renewable Energy) at the International Conference on Ocean, Offshore and Arctic Engineering (OMAE). Besides, she owns the Associate Fellowship of the Higher Education Academy (AFHEA). 


Assessment of wind turbine aero-hydro-servo-elastic modelling on the effects of mooring line tension via deep learning
Lin Zi, Liu Xiaolei
Energies Vol 13 (2020)
Wind power prediction based on High-frequency SCADA data along with isolation forest and deep learning neural networks
Lin Zi, Liu Xiaolei, Collu Maurizio
International Journal of Electrical Power & Energy Systems Vol 118 (2020)
A methodology to develop reduced-order models to support the operation and maintenance of offshore wind turbines
Lin Zi, Cevasco Debora, Collu Maurizio
Applied Energy (2019)
Investigation on PTO control of a combined axisymmetric buoy-WEC(CAB-WEC)
Kong Fankai, Su Weiming, Liu Hengxu, Collu Maurizio, Lin Zi, Chen Hailong, Zheng Xiongbo
Ocean Engineering Vol 188 (2019)
Progress on the development of a holistic coupled model of dynamics for offshore wind farms : phase II - study on a data-driven based reduced-order model for a single wind turbine
Lin Z, Stetco A, Carmona-Sanchez J, Cevasco D, Collu M, Nenadic G, Marjanovic O, Barnes M
38th International Conference on Ocean, Offshore & Arctic Engineering (2019)
An analysis of the impact of an advanced aero-hydro-servo-elastic model of dynamics on the generator-converter dynamics, for an offshore fixed 5MW PMSG wind turbine
Carmona-Sanchez J, Lin Z, Collu M, Barnes M, Marjanovic O, Cevasco D
15th IET International Conference on AC and DC Power Transmission (ACDC 2019) - Proceedings 15th IET International Conference on AC and DC Power Transmission, ACDC 2019 (2019)

more publications


Home Offshore: Holistic Operation and Maintenance for Energy Offshore Windfarms
Collu, Maurizio (Principal Investigator) Lin, Zi (Researcher) Xu, Xue (Researcher)
01-Jan-2018 - 20-Jan-2020
Holistic Operation and Maintenance for Energy from Offshore Wind Farms
Barnes, Mike (Principal Investigator) Collu, Maurizio (Co-investigator) Lin, Zi (Researcher) Cevasco, Debora (Post Grad Student)
HOME Offshore is a research project funded by the UK Engineering and Physical Sciences Research Council (EPSRC) which partners 5 leading UK universities. The project will investigate the use of advanced sensing, robotics, virtual reality models and artificial intelligence to reduce maintenance cost and effort for offshore windfarms. Predictive and diagnostic techniques will allow problems to be picked up early, when easy and inexpensive maintenance will allow problems to be readily fixed. Robots and advanced sensors will be used to minimise the need for human intervention in the hazardous offshore environment.

The remote inspection and asset management of offshore wind farms and their connection to shore, is an industry which will be worth up to £2 billion annually by 2025 in the UK alone. 80% to 90% of the cost of offshore Operation and Maintenance according to the Crown Estate is generated by access requirements: such as the need to get engineers and technicians to remote sites to evaluate a problem and decide what action to undertake. Such inspection takes place in a remote and hazardous environment and requires highly trained personnel, of which there is likely to be a shortage in coming years. Additionally much condition monitoring data which is presently generated is not useful or not used effectively.

The project therefore aims to make generate more ‘actionable data’ – useful information that can reduce operation and maintenance costs and improve safety.
02-Jan-2017 - 31-Jan-2020

more projects


Naval Architecture, Ocean and Marine Engineering
Henry Dyer Building

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