In the highly competitive world of renewables it is essential to get as much as possible from your assets. During this session we share our experience of utilizing Machine learning and AI to derive valuable insights from the data to enable proactive and focused maintenance in wind turbines.

Key Takeaways

How can detecting failures in wind turbines help in improving profitability?
How can ML/AI help with detecting failures in wind turbines?
What are the pit-falls with applying ML/AI at scale that one should be aware of?

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Speaker Bio

Pramod Bangalore is currently Head of Research at Greenbyte AB, a renewable energy tech company headquartered in Sweden. Pramod obtained his PhD in Electric Power Engineering from Chalmers University of Technology in 2016. His PhD demonstrated the use of Machine learning for condition monitoring of wind turbine components. Before he joined Greenbyte, he worked as a Big Data Applications expert in the Computer Science Dept. at Chalmers. His main interests lie in the industrial application of Machine learning and Artificial intelligence methods, especially in the renewable energy sector.

May 24 @ 13:20
13:20 — 13:50 (30′)

Stage 1 | Applied Data-Driven Maintenance

┬áProgram Day 2, Pramod Bangalore – Head of Research | Greenbyte