Presentation by Dr. Ravi Pandit: Digitalisation of offshore wind

Digitalisation of offshore wind

Due to the global energy crisis and environmental concerns, the wind has become an important renewable energy source and is expected to provide the most significant contribution to renewable energy targets for the EU and UK for 2020 and 2050. Offshore wind energy is a costly affair, and operation and maintenance (O&M) costs constitute a sizeable share of the total annual costs of a wind turbine (WT). Furthermore, as turbines get older, O&M cost is going to be significant which affects the financial profitability of wind industries. According to current statistics, the O&M costs average between $42,000 and $48,000/MW during the first 10 years of offshore WTs operations. The O&M cost further increases in case of unplanned maintenance caused by unexpected failures that increase the high chances of catastrophic failures that ultimately increases the downtime events and unplanned maintenance and logistic costs.

In this short presentation, I will showcase some of my research work that addresses these problems. More specifically, I would talk about probabilistic machine learning models and their application to condition monitoring, predictive maintenance, and big data computation, with the aim of reducing: 1) logistics costs & transport costs; 2) unscheduled maintenance; 3) catastrophic damages through developing advanced condition monitoring tools; 4) the number of inspections; 5) big data handling computational costs and 6) unavailability periods; while increasing availability and yield.  

Dr. Ravi Pandit is currently a Lecturer in Instrumentation and AI at the Center for Life-cycle Engineering and Management at the Cranfield University. With a robust academic journey, Dr. Pandit has held key positions, including Research Fellow at the University of Exeter (2020-2021), Research Associate at the University of Strathclyde (2016-2020), and Visiting Researcher roles at UCLM Spain (2018) and Wood plc Scotland (2016-2017). Dr Pandit academic contributions extend to Assistant Professor roles at Jadavpur University (Electronics and Instrumentation Engineering, 2014-2016) and VIT Vellore (School of Electrical Engineering, 2011-2014). Dr Pandit worked on a number of internationally collaborated EU Horizon (e.g, ROMEO, AWESOME), and Alan Turing projects. Also, During his professional career, he received a number of prestigious and highly competitive research Grants and fellowships, for example, Tactical Fund (DSIT, UK), Research Acceleration Fellowship (HEF, UK), Travel Grant (NREL, USA), Marie Curie Fellowship (EU Horizon), Erasmus Mundus (EU). 

As of now, has published numerous papers in highly ranked journals (mostly in Q1 & Q2) and have presented my research at several international conferences and workshops in these areas. Furthermore, He achieved the impressive rank of #4 globally in the field of condition monitoring according to ScholarGPS. Dr. Pandit is a leading expert in clean energy digitization, climate change, and environmental applications of data-driven technologies. Notably, Dr. Pandit research interests encompass:

• Data analytics/ML for clean energy technologies (offshore winds, solar)
• Probabilistic machine learning, Bayesian non-parametric predictive models (e.g., Gaussian Process)
• Predictive maintenance & condition monitoring
• Forecasting and Prediction: long-term and short-term
• Big data statistical analysis: time-series, modelling
• CFD modelling & wind farm optimisations.